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bug fixes for convert-train-checkpoint-to-gguf

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xaedes 2023-08-28 17:57:10 +02:00
parent c690c20362
commit 5f27ade48e
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1 changed files with 91 additions and 89 deletions
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180
examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py
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@ -4,7 +4,9 @@
import argparse import argparse
import gguf import gguf
import os import os
import struct
import sys import sys
import numpy as np
from pathlib import Path from pathlib import Path
# gguf constants # gguf constants
@ -55,7 +57,10 @@ class Tensor:
self.ne = ne self.ne = ne
self.nbytes = 0 self.nbytes = 0
if self.dtype == 'f': if self.dtype == 'f':
self.nbytes = product(self.ne) * 4 if len(self.ne) == 0:
self.nbytes = 0
else:
self.nbytes = int(np.product(self.ne)) * 4
else: else:
raise ValueError(f"Unhandled data type '{self.dtype}'") raise ValueError(f"Unhandled data type '{self.dtype}'")
@ -67,7 +72,7 @@ class Tensor:
assert(nd == len(self.ne)) assert(nd == len(self.ne))
ne = [] ne = []
for d in range(nd): for d in range(nd):
n = struct.unpack('<Q', bytes(data[offset:offset + 8]))[0]; offset += 8 n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
ne.append(n) ne.append(n)
assert(tuple(ne) == tuple(self.ne)) assert(tuple(ne) == tuple(self.ne))
@ -81,7 +86,7 @@ class Tensor:
# 32-byte alignment # 32-byte alignment
offset += (0 - offset) & 31 offset += (0 - offset) & 31
self.data = data[offset:offset+self.nbytes] self.data = data[offset:offset+self.nbytes]
offset += self.nbytes
return offset return offset
def max_storage_size(self): def max_storage_size(self):
@ -100,7 +105,7 @@ class Tensor:
name=name, name=name,
tensor=self.data, tensor=self.data,
raw_shape=np.array(list(reversed(self.ne))), raw_shape=np.array(list(reversed(self.ne))),
raw_type=gguf.GGMLQuantizationType.F32) raw_dtype=gguf.GGMLQuantizationType.F32)
class OptimizationParamsV0: class OptimizationParamsV0:
def __init__(self): def __init__(self):
@ -141,26 +146,26 @@ class OptimizationContext:
self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0] self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
offset += 4 offset += 4
if version == 0: if self.version == 0:
params = OptimizationParamsV0() params = OptimizationParamsV0()
offset += params.load(data, offset) offset = params.load(data, offset)
self.past = params.past self.past = params.past
self.lbfgs_m = params.lbfgs_m self.lbfgs_m = params.lbfgs_m
self.nx = struct.unpack('<Q', bytes(data[offset:offset + 8]))[0]; offset += 8 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.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.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
self.type = params.type self.type = params.type
self.adam_m = Tensor('f', [self.nx]) self.adam_m = Tensor('f', [self.nx])
self.adam_v = Tensor('f', [self.nx]) self.adam_v = Tensor('f', [self.nx])
self.adam_pf = Tensor('f', [self.past]) self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_x = Tensor('f', [self.nx]) self.lbfgs_x = Tensor('f', [self.nx])
self.lbfgs_xp = Tensor('f', [self.nx]) self.lbfgs_xp = Tensor('f', [self.nx])
self.lbfgs_g = Tensor('f', [self.nx]) self.lbfgs_g = Tensor('f', [self.nx])
self.lbfgs_gp = Tensor('f', [self.nx]) self.lbfgs_gp = Tensor('f', [self.nx])
self.lbfgs_d = Tensor('f', [self.nx]) self.lbfgs_d = Tensor('f', [self.nx])
self.lbfgs_pf = Tensor('f', [self.past]) self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_lmal = Tensor('f', [self.lbfgs_m]) self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
self.lbfgs_lmys = 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_lms = Tensor('f', [self.nx, self.lbfgs_m])
@ -174,32 +179,30 @@ class OptimizationContext:
mh = Tensor('f', [self.nx]) mh = Tensor('f', [self.nx])
mv = Tensor('f', [self.nx]) mv = Tensor('f', [self.nx])
offset += x.load(data, offset) offset = x.load(data, offset)
offset += g.load(data, offset) offset = g.load(data, offset)
offset += g2.load(data, offset) offset = g2.load(data, offset)
offset += self.adam_m.load(data, offset) offset = self.adam_m.load(data, offset)
offset += self.adam_v.load(data, offset) offset = self.adam_v.load(data, offset)
offset += mh.load(data, offset) offset = mh.load(data, offset)
offset += vh.load(data, offset) offset = vh.load(data, offset)
if self.past > 0: offset = self.adam_pf.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_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_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 self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
elif self.type == 1: elif self.type == 1:
offset += self.lbfgs_x.load(data, offset) offset = self.lbfgs_x.load(data, offset)
offset += self.lbfgs_xp.load(data, offset) offset = self.lbfgs_xp.load(data, offset)
offset += self.lbfgs_g.load(data, offset) offset = self.lbfgs_g.load(data, offset)
offset += self.lbfgs_gp.load(data, offset) offset = self.lbfgs_gp.load(data, offset)
offset += self.lbfgs_d.load(data, offset) offset = self.lbfgs_d.load(data, offset)
if self.past > 0: offset = self.lbfgs_pf.load(data, offset)
offset += self.lbfgs_pf.load(data, offset) offset = self.lbfgs_lmal.load(data, offset)
offset += self.lbfgs_lmal.load(data, offset) offset = self.lbfgs_lmys.load(data, offset)
offset += self.lbfgs_lmys.load(data, offset) offset = self.lbfgs_lms.load(data, offset)
offset += self.lbfgs_lms.load(data, offset) offset = self.lbfgs_lmy.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_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_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
@ -212,23 +215,23 @@ class OptimizationContext:
raise ValueError('Unknown optimizer type') raise ValueError('Unknown optimizer type')
elif version == 1: elif self.version == 1:
self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4 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.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.nx = struct.unpack('<Q', bytes(data[offset:offset + 8]))[0]; offset += 8 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.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.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
self.adam_m = Tensor('f', [self.nx]) self.adam_m = Tensor('f', [self.nx])
self.adam_w = Tensor('f', [self.nx]) self.adam_v = Tensor('f', [self.nx])
self.adam_pf = Tensor('f', [self.past]) self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_x = Tensor('f', [self.nx]) self.lbfgs_x = Tensor('f', [self.nx])
self.lbfgs_xp = Tensor('f', [self.nx]) self.lbfgs_xp = Tensor('f', [self.nx])
self.lbfgs_g = Tensor('f', [self.nx]) self.lbfgs_g = Tensor('f', [self.nx])
self.lbfgs_gp = Tensor('f', [self.nx]) self.lbfgs_gp = Tensor('f', [self.nx])
self.lbfgs_d = Tensor('f', [self.nx]) self.lbfgs_d = Tensor('f', [self.nx])
self.lbfgs_pf = Tensor('f', [self.past]) self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_lmal = Tensor('f', [self.lbfgs_m]) self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
self.lbfgs_lmys = 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_lms = Tensor('f', [self.nx, self.lbfgs_m])
@ -237,14 +240,14 @@ class OptimizationContext:
# forgot to save type in version 1: # forgot to save type in version 1:
# guess self.type from number of remaining bytes # guess self.type from number of remaining bytes
size_type_0 = 12 + sum([t.max_storage_size() for t in size_type_0 = 12 + sum([t.max_storage_size() for t in
[self.adam_m, self.adam_w] [self.adam_m, self.adam_v]
+[self.adam_pf] if self.past > 0 else []]) +([self.adam_pf] if (self.past > 0) else [])])
size_type_1 = 24 + sum([t.max_storage_size() for t in size_type_1 = 24 + sum([t.max_storage_size() for t in
[self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g, [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf, self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
self.lbfgs_lmal, self.lbfgs_lmys, self.lbfgs_lmal, self.lbfgs_lmys,
self.lbfgs_lms, self.lbfgs_lmy] self.lbfgs_lms, self.lbfgs_lmy]
+[self.lbfgs_pf] if self.past > 0 else []]) +([self.lbfgs_pf] if (self.past > 0) else [])])
# due to alignment padding the size might not by exact # due to alignment padding the size might not by exact
# but the difference in size for both types is significant, # but the difference in size for both types is significant,
# so we can just use whichever is closest # so we can just use whichever is closest
@ -255,28 +258,25 @@ class OptimizationContext:
self.type = 1 self.type = 1
if self.type == 0: if self.type == 0:
offset += self.adam_m.load(data, offset) offset = self.adam_m.load(data, offset)
offset += self.adam_w.load(data, offset) offset = self.adam_v.load(data, offset)
if self.past > 0: offset = self.adam_pf.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_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_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 self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
elif self.type == 1: elif self.type == 1:
offset = self.lbfgs_x.load(data, offset)
offset += self.lbfgs_x.load(data, offset) offset = self.lbfgs_xp.load(data, offset)
offset += self.lbfgs_xp.load(data, offset) offset = self.lbfgs_g.load(data, offset)
offset += self.lbfgs_g.load(data, offset) offset = self.lbfgs_gp.load(data, offset)
offset += self.lbfgs_gp.load(data, offset) offset = self.lbfgs_d.load(data, offset)
offset += self.lbfgs_d.load(data, offset) offset = self.lbfgs_pf.load(data, offset)
if self.past > 0: offset = self.lbfgs_lmal.load(data, offset)
offset += self.lbfgs_pf.load(data, offset) offset = self.lbfgs_lmys.load(data, offset)
offset += self.lbfgs_lmal.load(data, offset) offset = self.lbfgs_lms.load(data, offset)
offset += self.lbfgs_lmys.load(data, offset) offset = self.lbfgs_lmy.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_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_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
@ -295,7 +295,7 @@ class OptimizationContext:
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past) 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_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter) gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized) gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
if self.type == 0: if self.type == 0:
gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM) gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
@ -357,6 +357,9 @@ class ModelParams:
gguf_writer.add_rope_dimension_count(self.n_rot) gguf_writer.add_rope_dimension_count(self.n_rot)
gguf_writer.add_feed_forward_length(self.get_n_ff()) gguf_writer.add_feed_forward_length(self.get_n_ff())
def tensor_name(key, bid=None):
return gguf.MODEL_TENSOR_NAMES[gguf.MODEL_ARCH.LLAMA][key].format(bid=bid) + ".weight"
class Layer: class Layer:
def __init__(self, params, bid): def __init__(self, params, bid):
self.bid = bid self.bid = bid
@ -371,27 +374,27 @@ class Layer:
self.w3 = Tensor('f', [params.n_embd, params.get_n_ff()]) self.w3 = Tensor('f', [params.n_embd, params.get_n_ff()])
def load(self, data, offset): def load(self, data, offset):
offset += self.att_norm.load(data, offset) offset = self.att_norm.load(data, offset)
offset += self.wq.load(data, offset) offset = self.wq.load(data, offset)
offset += self.wk.load(data, offset) offset = self.wk.load(data, offset)
offset += self.wv.load(data, offset) offset = self.wv.load(data, offset)
offset += self.wo.load(data, offset) offset = self.wo.load(data, offset)
offset += self.ffn_norm.load(data, offset) offset = self.ffn_norm.load(data, offset)
offset += self.w1.load(data, offset) offset = self.w1.load(data, offset)
offset += self.w2.load(data, offset) offset = self.w2.load(data, offset)
offset += self.w3.load(data, offset) offset = self.w3.load(data, offset)
return offset return offset
def save_gguf(self, gguf_writer): def save_gguf(self, gguf_writer):
self.att_norm.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.ATTN_NORM].format(bid=self.bid)) self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
self.wq.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.ATTN_Q].format(bid=self.bid)) self.wq.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q, self.bid))
self.wk.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.ATTN_K].format(bid=self.bid)) self.wk.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K, self.bid))
self.wv.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.ATTN_V].format(bid=self.bid)) self.wv.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V, self.bid))
self.wo.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.ATTN_OUT].format(bid=self.bid)) self.wo.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, self.bid))
self.ffn_norm.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.FFN_NORM].format(bid=self.bid)) self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM, self.bid))
self.w1.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.FFN_GATE].format(bid=self.bid)) self.w1.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE, self.bid))
self.w2.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.FFN_DOWN].format(bid=self.bid)) self.w2.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, self.bid))
self.w3.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.FFN_UP].format(bid=self.bid)) self.w3.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP, self.bid))
class Model: class Model:
def __init__(self): def __init__(self):
@ -405,14 +408,14 @@ class Model:
self.norm = Tensor('f', [self.params.n_embd]) self.norm = Tensor('f', [self.params.n_embd])
self.output = Tensor('f', [self.params.n_embd, self.params.n_vocab]) self.output = Tensor('f', [self.params.n_embd, self.params.n_vocab])
offset += self.tok_embd.load(data, offset) offset = self.tok_embd.load(data, offset)
offset += self.norm.load(data, offset) offset = self.norm.load(data, offset)
offset += self.output.load(data, offset) offset = self.output.load(data, offset)
self.layers.clear() self.layers.clear()
for bid in range(self.n_layer): for bid in range(self.params.n_layer):
layer = Layer(self.params, bid) layer = Layer(self.params, bid)
offset += layer.load(data, offset) offset = layer.load(data, offset)
self.layers.append(layer) self.layers.append(layer)
return offset return offset
@ -420,9 +423,9 @@ class Model:
def save_gguf(self, gguf_writer): def save_gguf(self, gguf_writer):
self.params.save_gguf(gguf_writer) self.params.save_gguf(gguf_writer)
self.tok_embd.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.TOKEN_EMBD]) self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
self.norm.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.OUTPUT_NORM]) self.norm.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
self.output.save_gguf(gguf_writer, name=gguf.MODEL_TENSOR_NAMES[gguf_writer.arch][gguf.MODEL_TENSOR.OUTPUT]) self.output.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
for layer in self.layers: for layer in self.layers:
layer.save_gguf(gguf_writer) layer.save_gguf(gguf_writer)
@ -433,25 +436,24 @@ class Checkpoint:
self.opt_ctx = OptimizationContext() self.opt_ctx = OptimizationContext()
def load(self, data, offset): def load(self, data, offset):
magic = bytes(data[offset:offset + 4]); offset += 4 magic = bytes(reversed(data[offset:offset + 4])); offset += 4
if magic != b'ggcp': if magic != b'ggcp':
raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'") raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
if version != 0: if self.version != 0:
raise ValueError('Invalid version of checkpoint file') raise ValueError('Invalid version of checkpoint file')
self.train_its = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4 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_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 self.train_tokens = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
offset += self.model.load(data, offset) offset = self.model.load(data, offset)
offset += self.opt_ctx.load(data, offset) offset = self.opt_ctx.load(data, offset)
return offset return offset
def save_gguf(self, gguf_writer): def save_gguf(self, gguf_writer):
gguf_writer.add_architecture()
gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32) gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
gguf_writer.add_layer_norm_rms_eps(1e-5) gguf_writer.add_layer_norm_rms_eps(1e-5)
gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION, 0) gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION, 0)
@ -463,8 +465,8 @@ class Checkpoint:
def handle_args(): def handle_args():
parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF') parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
parser.add_argument('--input', '-i', type = Path, help = 'Input train checkpoint filename') parser.add_argument('--input', '-i', type = Path, help = 'Input train checkpoint filename', required=True)
parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename') parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
return parser.parse_args() return parser.parse_args()
def main(): def main():
@ -476,7 +478,7 @@ def main():
# we should have read all available data # we should have read all available data
assert(offset == len(data)) assert(offset == len(data))
gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False) gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
chk.save_gguf(gguf_writer) chk.save_gguf(gguf_writer)
print(" gguf: write header") print(" gguf: write header")
gguf_writer.write_header_to_file() gguf_writer.write_header_to_file()