Bug fixes
This commit is contained in:
Daniel Han
parent
7fe2874157
commit
be660d3bb1
4 changed files with 37 additions and 91 deletions
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@ -20,6 +20,7 @@ __all__ = [
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"to_sharegpt",
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"standardize_sharegpt",
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"standardize_data_formats",
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"apply_chat_template",
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"train_on_responses_only",
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@ -37,7 +38,9 @@ from .models._utils import patch_tokenizer
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import re
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from unsloth_zoo.dataset_utils import (
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train_on_responses_only,
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standardize_data_formats,
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)
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standardize_sharegpt = standardize_data_formats
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CHAT_TEMPLATES = {}
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DEFAULT_SYSTEM_MESSAGE = {}
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@ -1474,90 +1477,6 @@ def to_sharegpt(
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pass
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def standardize_sharegpt(
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dataset,
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aliases_for_system = ["system",],
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aliases_for_user = ["user", "human", "input",],
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aliases_for_assistant = ["gpt", "assistant", "output",],
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):
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"""
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Standardizes ShareGPT and other formats to user/assistant Hugging Face format.
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Get aliases for the system, user and assistant roles.
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These shall map to "system", "user" and "assistant" respectively.
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aliases_for_system = ["system",],
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aliases_for_user = ["user", "human", "input",],
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aliases_for_assistant = ["gpt", "assistant", "output",],
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"""
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import collections
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import itertools
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convos = dataset[:10]["conversations"]
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uniques = collections.defaultdict(list)
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for convo in convos:
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for message in convo:
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for key, value in message.items():
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uniques[key].append(value)
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pass
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# Must be only 2 entries
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assert(len(uniques.keys()) == 2)
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keys = list(uniques.keys())
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length_first = len(set(uniques[keys[0]]))
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length_second = len(set(uniques[keys[1]]))
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if length_first < length_second:
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# Role is assigned to the first element
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role_key = keys[0]
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content_key = keys[1]
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else:
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role_key = keys[1]
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content_key = keys[0]
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pass
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# Check roles are in aliases
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all_aliases = set(aliases_for_system + aliases_for_user + aliases_for_assistant)
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roles = set(uniques[role_key])
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leftover_aliases = (all_aliases | roles) - all_aliases
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if len(leftover_aliases) != 0:
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raise TypeError(
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f"Unsloth: {list(leftover_aliases)} are not in aliases. Please update aliases."
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)
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pass
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# Mapping for aliases
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aliases_mapping = {}
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for x in aliases_for_system: aliases_mapping[x] = "system"
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for x in aliases_for_user: aliases_mapping[x] = "user"
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for x in aliases_for_assistant: aliases_mapping[x] = "assistant"
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def _standardize_dataset(examples):
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convos = examples["conversations"]
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all_convos = []
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for convo in convos:
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new_convo = [
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{ "role" : aliases_mapping[message[role_key]], "content" : message[content_key], }
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for message in convo
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]
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all_convos.append(new_convo)
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pass
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return { "conversations" : all_convos, }
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pass
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from multiprocessing import cpu_count
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num_proc = cpu_count()
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return dataset.map(
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_standardize_dataset,
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batched = True,
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desc = "Unsloth: Standardizing formats",
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num_proc = num_proc,
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)
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pass
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def get_ollama_eos_tokens(tokenizer, extra_eos_tokens = []):
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added_tokens_decoder = tokenizer.added_tokens_decoder.values()
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added_tokens_decoder = [str(x) for x in added_tokens_decoder]
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@ -38,6 +38,7 @@ from ..kernels import *
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from ..tokenizer_utils import *
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if HAS_FLASH_ATTENTION:
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from flash_attn import flash_attn_func
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from .vision import FastBaseModel
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# Final patching code
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from transformers.models.llama.modeling_llama import (
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@ -1648,6 +1649,7 @@ class FastLlamaModel:
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disable_log_stats = False,
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**kwargs,
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):
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os.environ["UNSLOTH_USE_NEW_MODEL"] = "0"
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if trust_remote_code:
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if fast_inference:
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raise NotImplementedError("Unsloth: Fast inference does not support `trust_remote_code` yet.")
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@ -2016,6 +2018,31 @@ class FastLlamaModel:
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temporary_location = "_unsloth_temporary_saved_buffers",
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**kwargs,
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):
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if os.environ.get("UNSLOTH_USE_NEW_MODEL", "0") == "1":
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return FastBaseModel.get_model(
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model = model,
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r = r,
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target_modules = target_modules,
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lora_alpha = lora_alpha,
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lora_dropout = lora_dropout,
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bias = bias,
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finetune_vision_layers = False,
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finetune_language_layers = True,
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finetune_attention_modules = True,
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finetune_mlp_modules = True,
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layers_to_transform = layers_to_transform,
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layers_pattern = layers_pattern,
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use_gradient_checkpointing = use_gradient_checkpointing,
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random_state = random_state,
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max_seq_length = max_seq_length,
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use_rslora = use_rslora,
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modules_to_save = modules_to_save,
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init_lora_weights = init_lora_weights,
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loftq_config = loftq_config,
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temporary_location = temporary_location,
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**kwargs,
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)
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pass
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if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1":
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print("Unsloth: Full finetuning is enabled, so .get_peft_model has no effect")
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return model
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@ -70,7 +70,7 @@ class FastLanguageModel(FastLlamaModel):
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@staticmethod
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def from_pretrained(
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model_name = "unsloth/Llama-3.2-1B-Instruct",
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max_seq_length = None,
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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load_in_8bit = False,
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@ -96,7 +96,7 @@ class FastLanguageModel(FastLlamaModel):
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if load_in_8bit or full_finetuning:
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return FastModel.from_pretrained(
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model_name = model_name,
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max_seq_length = max_seq_length, # [TODO] No effect
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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load_in_8bit = load_in_8bit,
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@ -295,7 +295,7 @@ class FastLanguageModel(FastLlamaModel):
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else:
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return FastModel.from_pretrained(
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model_name = model_name,
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max_seq_length = max_seq_length, # [TODO] No effect
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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load_in_8bit = load_in_8bit,
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@ -442,7 +442,7 @@ class FastModel(FastBaseModel):
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@staticmethod
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def from_pretrained(
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model_name = "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
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max_seq_length = None, # [TODO] No effect
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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load_in_8bit = False,
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@ -668,7 +668,7 @@ class FastModel(FastBaseModel):
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use_gradient_checkpointing = use_gradient_checkpointing,
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*args, **kwargs,
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)
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if resize_model_vocab is not None:
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model.resize_token_embeddings(resize_model_vocab)
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pass
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@ -25,7 +25,6 @@ try:
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except:
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from transformers import AutoModelForVision2Seq
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pass
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from .llama import *
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from ..kernels import (
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post_patch_loss_function,
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)
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@ -100,7 +99,7 @@ class FastBaseModel:
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@staticmethod
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def from_pretrained(
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model_name = "unsloth/Llama-3.2-1B-Instruct",
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max_seq_length = None,
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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load_in_8bit = False,
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@ -114,6 +113,7 @@ class FastBaseModel:
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use_gradient_checkpointing = "unsloth",
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**kwargs,
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):
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os.environ["UNSLOTH_USE_NEW_MODEL"] = "1"
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if trust_remote_code:
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print(
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"Unsloth: WARNING `trust_remote_code` is True.\n"\
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