mirror of
https://github.com/ggerganov/llama.cpp.git
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3fd62a6b1c
* py : type-check all Python scripts with Pyright * server-tests : use trailing slash in openai base_url * server-tests : add more type annotations * server-tests : strip "chat" from base_url in oai_chat_completions * server-tests : model metadata is a dict * ci : disable pip cache in type-check workflow The cache is not shared between branches, and it's 250MB in size, so it would become quite a big part of the 10GB cache limit of the repo. * py : fix new type errors from master branch * tests : fix test-tokenizer-random.py Apparently, gcc applies optimisations even when pre-processing, which confuses pycparser. * ci : only show warnings and errors in python type-check The "information" level otherwise has entries from 'examples/pydantic_models_to_grammar.py', which could be confusing for someone trying to figure out what failed, considering that these messages can safely be ignored even though they look like errors.
488 lines
27 KiB
Python
488 lines
27 KiB
Python
#!/usr/bin/env python3
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# finetune checkpoint --> gguf conversion
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import argparse
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import gguf
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import struct
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import numpy as np
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from pathlib import Path
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# gguf constants
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LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
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LLM_KV_OPTIMIZER_TYPE_ADAM = "adam"
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LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
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LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version"
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LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count"
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LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count"
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LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count"
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LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized"
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LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss"
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LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss"
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LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count"
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LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
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LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss"
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LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step"
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LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j"
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LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k"
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LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end"
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LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
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LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments"
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LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments"
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LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
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LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters"
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LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
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LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients"
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LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients"
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LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction"
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LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values"
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LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha"
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LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys"
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LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s"
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LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y"
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LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model"
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LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora"
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LLM_KV_TRAINING_TYPE = "training.type"
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LLM_KV_TRAINING_FILE_VERSION = "training.file_version"
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LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"
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LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count"
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LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count"
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LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = "training.lora.rank.token_embd"
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LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm"
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LLM_KV_TRAINING_LORA_RANK_OUTPUT = "training.lora.rank.output"
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LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = "training.lora.rank.attn_norm"
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LLM_KV_TRAINING_LORA_RANK_ATTN_Q = "training.lora.rank.attn_q"
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LLM_KV_TRAINING_LORA_RANK_ATTN_K = "training.lora.rank.attn_k"
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LLM_KV_TRAINING_LORA_RANK_ATTN_V = "training.lora.rank.attn_v"
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LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = "training.lora.rank.attn_output"
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LLM_KV_TRAINING_LORA_RANK_FFN_NORM = "training.lora.rank.ffn_norm"
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LLM_KV_TRAINING_LORA_RANK_FFN_GATE = "training.lora.rank.ffn_gate"
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LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = "training.lora.rank.ffn_down"
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LLM_KV_TRAINING_LORA_RANK_FFN_UP = "training.lora.rank.ffn_up"
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class Tensor:
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def __init__(self, dtype='f', ne=None):
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if ne is None:
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ne = []
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self.dtype = dtype
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self.ne = ne
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self.nbytes = 0
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if self.dtype == 'f':
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if len(self.ne) == 0:
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self.nbytes = 0
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else:
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self.nbytes = int(np.prod(self.ne)) * 4
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else:
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raise ValueError(f"Unhandled data type '{self.dtype}'")
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def load(self, data, offset):
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nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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assert(nd == len(self.ne))
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ne = []
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for d in range(nd):
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n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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ne.append(n)
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if tuple(ne) != tuple(self.ne):
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raise ValueError(f"Tensor.load: Expected number of elements {str(self.ne)} does not match what is read from file {str(ne)}")
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if self.dtype == 'f':
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assert(dtype == 0)
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else:
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raise ValueError(f"Unhandled data type '{self.dtype}'")
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self.name = bytes(data[offset:offset+namelen]); offset += namelen
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# 32-byte alignment
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offset += (0 - offset) & 31
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self.data = data[offset:offset+self.nbytes]
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offset += self.nbytes
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return offset
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def max_storage_size(self):
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result = 0
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result += 4 # nd
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result += 4 # namelen
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result += 4 # dtype
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result += len(self.ne)*8 # ne
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result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
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result += 31 # 32-byte alignment
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result += self.nbytes
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return result
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def save_gguf(self, gguf_writer, name):
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gguf_writer.add_tensor(
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name=name,
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tensor=self.data,
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raw_shape=np.array(list(reversed(self.ne))),
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raw_dtype=gguf.GGMLQuantizationType.F32)
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class OptimizationContext:
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def __init__(self):
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pass
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def load(self, data, offset):
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self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
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offset += 4
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if self.version != 1:
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raise ValueError('Invalid version of optimization context in checkpoint file')
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self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0]; offset += 8
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self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
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self.adam_m = Tensor('f', [self.nx])
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self.adam_v = Tensor('f', [self.nx])
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self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
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self.lbfgs_x = Tensor('f', [self.nx])
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self.lbfgs_xp = Tensor('f', [self.nx])
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self.lbfgs_g = Tensor('f', [self.nx])
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self.lbfgs_gp = Tensor('f', [self.nx])
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self.lbfgs_d = Tensor('f', [self.nx])
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self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
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self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
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self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
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self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m])
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self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m])
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# forgot to save type in version 1:
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# guess self.type from number of remaining bytes
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size_type_0 = 12 + sum([t.max_storage_size() for t in
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[self.adam_m, self.adam_v]
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+([self.adam_pf] if (self.past > 0) else [])])
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size_type_1 = 24 + sum([t.max_storage_size() for t in
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[self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
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self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
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self.lbfgs_lmal, self.lbfgs_lmys,
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self.lbfgs_lms, self.lbfgs_lmy]
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+([self.lbfgs_pf] if (self.past > 0) else [])])
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# due to alignment padding the size might not by exact
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# but the difference in size for both types is significant,
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# so we can just use whichever is closest
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remaining = len(data) - offset
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if abs(remaining - size_type_0) < abs(remaining - size_type_1):
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self.type = 0
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else:
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self.type = 1
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if self.type == 0:
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offset = self.adam_m.load(data, offset)
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offset = self.adam_v.load(data, offset)
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offset = self.adam_pf.load(data,offset)
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self.adam_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.adam_fx_prev = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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elif self.type == 1:
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offset = self.lbfgs_x.load(data, offset)
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offset = self.lbfgs_xp.load(data, offset)
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offset = self.lbfgs_g.load(data, offset)
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offset = self.lbfgs_gp.load(data, offset)
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offset = self.lbfgs_d.load(data, offset)
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offset = self.lbfgs_pf.load(data, offset)
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offset = self.lbfgs_lmal.load(data, offset)
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offset = self.lbfgs_lmys.load(data, offset)
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offset = self.lbfgs_lms.load(data, offset)
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offset = self.lbfgs_lmy.load(data, offset)
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self.lbfgs_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.lbfgs_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.lbfgs_j = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.lbfgs_k = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.lbfgs_end = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
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else:
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raise ValueError(f"Invalid optimizer type '{self.type}'")
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return offset
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def save_gguf(self, gguf_writer):
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gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
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gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
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gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
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gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
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gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
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if self.type == 0:
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gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
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gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
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gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
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gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
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self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
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self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
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if self.past > 0:
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self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
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elif self.type == 1:
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gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
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gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
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gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
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gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
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gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
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gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
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gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
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gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
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self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
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self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
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self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
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self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
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self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
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if self.past > 0:
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self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
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self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
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self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
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self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
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self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
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else:
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raise ValueError('Unknown optimizer type')
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class LoraParams:
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def __init__(self):
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pass
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def load(self, data, offset):
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self.n_rank_attention_norm = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_wq = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_wk = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_wv = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_wo = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_ffn_norm = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_w1 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_w2 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_w3 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_tok_embeddings = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_norm = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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self.n_rank_output = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
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return offset
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def save_gguf(self, gguf_writer):
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD, self.n_rank_tok_embeddings)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, self.n_rank_norm)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT, self.n_rank_output)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_NORM, self.n_rank_attention_norm)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_Q, self.n_rank_wq)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_K, self.n_rank_wk)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_V, self.n_rank_wv)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, self.n_rank_wo)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_NORM, self.n_rank_ffn_norm)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_GATE, self.n_rank_w1)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, self.n_rank_w2)
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gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_UP, self.n_rank_w3)
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class ModelParams:
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def __init__(self, n_ff = None):
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self.n_ff = n_ff
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def load(self, data, offset):
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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()
|