Bug fixes (#3329)
* Fix mamba * Update loader.py * Update vision.py * Update loader.py * Filter vLLM standby logs (#3131) * filter vLLM standby logs * safeguard standby logger patch * Update unsloth/models/_utils.py * Update unsloth/models/_utils.py * Update unsloth/models/_utils.py --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Update loader.py * Add scaler * Update llama.py * Update _utils.py * Versioning * GPT OSS fix * GPT OSS fix * Update loader.py * Update vision.py * Update vision.py * Update loader.py * Update vision.py * Update vision.py * Update llama.py * Update llama.py * Update llama.py * Versioning * Update mapper.py * Update vision.py * Update vision.py * Update vision.py * Upcast norms * Update loader.py * Update vision.py * Upcast layernorms * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update save.py * Update rl.py * Update pyproject.toml * Update rl.py * Update rl_replacements.py * Update rl.py * Update rl.py * Update rl.py * Update _utils.py * Update __init__.py * Torch 2.8 * Update rl_replacements.py * Update loader.py * UNSLOTH_ENABLE_CCE * Fix * Update loader.py * Update loader.py * Update __init__.py * Update __init__.py * Update __init__.py * Update __init__.py * Import fixes * Update loader.py * Fix aimv2 issue * Update loader.py * Update import_fixes.py * Update import_fixes.py * Update loader.py * Update loader.py * Update loader.py * Upgrade * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update vision.py * Update vision.py * custom_datatype * recheck * Float16 * Update vision.py * Update vision.py * Update vision.py * Update vision.py * Update vision.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Bug fix * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Update loader.py * torch_dtype * Update rl.py * Fix CE Loss * Versioning * Update loader.py * Update loader.py * extract_model_type_from_config * Model types * Update loader.py * get_transformers_model_type * Update loader.py * Update loader.py * Update loader.py * Update rl.py * Update pyproject.toml * Update loader.py * Update loader.py * Update loader.py * Update loader.py * Versioning * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update vision.py * Update vision.py * Fix DataParallel * Update _utils.py * Update rl.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py * Update synthetic.py --------- Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
This commit is contained in:
Daniel Han
GitHub
parent
1b3fdd5565
commit
70f790a8e4
4 changed files with 30 additions and 21 deletions
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@ -28,6 +28,7 @@ from unsloth_zoo.vllm_utils import (
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patch_vllm,
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delete_vllm,
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)
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from unsloth_zoo.log import logger
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import numpy as np
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from .synthetic_configs import (
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@ -76,6 +77,7 @@ class SyntheticDataKit:
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return_args = True,
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enable_lora = False,
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use_bitsandbytes = False,
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compilation_config = 3,
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**kwargs,
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)
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if "dtype" in engine_args:
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@ -95,15 +97,17 @@ class SyntheticDataKit:
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engine_args["dtype"] = "auto"
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if "device" in engine_args: del engine_args["device"]
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if "model" in engine_args: del engine_args["model"]
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if "compilation_config" in engine_args:
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# Cannot parse in vllm serve
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engine_args["compilation_config"] = 3
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subprocess_commands = [
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"vllm", "serve", str(model_name),
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]
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for key, value in engine_args.items():
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flag = key.replace("_", "-")
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flag = key.replace("_", "-")
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if key == "compilation_config":
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# [TODO] Unsure why subprocess doesn't process json properly
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# Also -O3 breaks on T4!
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# subprocess_commands += ["-O3",]
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continue
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which = str(value).replace("torch.", "")
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if which == "True":
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# Ignore --enforce-eager True
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@ -117,6 +121,7 @@ class SyntheticDataKit:
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else:
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subprocess_commands += ["--" + flag, which,]
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pass
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logger.info(subprocess_commands)
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vllm_process = subprocess.Popen(
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subprocess_commands,
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stdout = subprocess.PIPE,
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@ -1200,7 +1200,8 @@ def CausalLM_fast_forward(fast_forward_inference):
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if not RETURN_LOGITS and labels is not None:
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n_items = kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None)
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n_items = kwargs.get("num_items_in_batch", None)
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if n_items is None: n_items = kwargs.get("n_items", None)
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if self.config.model_type == "falcon_h1":
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hidden_states = hidden_states * self.config.lm_head_multiplier
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@ -1264,12 +1265,14 @@ def CausalLM_fast_forward(fast_forward_inference):
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shift_labels[..., :-1] = labels[..., 1:]
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shift_labels[..., -1] = -100
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# shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
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n_items = kwargs.get("num_items_in_batch", None)
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if n_items is None: n_items = kwargs.get("n_items", None)
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loss = fast_cross_entropy_loss(
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logits = shift_logits,
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labels = shift_labels,
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logit_softcapping = logit_softcapping,
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logit_scaling = logit_scaling,
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n_items = kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None),
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n_items = n_items,
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)
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else:
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if logit_scaling != 0:
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@ -114,6 +114,7 @@ import numpy as np
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from contextlib import nullcontext
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from torch.nn import functional as F
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from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
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from transformers.training_args import ParallelMode
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torch_compile_options = {{
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"epilogue_fusion" : True,
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@ -168,6 +169,11 @@ class Unsloth{RLTrainer_name}(_Unsloth{RLTrainer_name}):
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):
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if args is None: args = Unsloth{RLConfig_name}()
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{RLTrainer_extra_args}
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# [TODO] Fix up DataParallel multiplying batch sizes
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# [TODO] DDP works, but DP seems to not work? [TODO]
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if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
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if getattr(args, "_n_gpu", 1) != 1:
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args._n_gpu = 1
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super().__init__({RLTrainer_call_args}{RLTrainer_kwargs})
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{RLTrainer_post}
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pass
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@ -265,14 +271,17 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
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"if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')\n"\
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"if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')\n"\
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"if force_float32:\n"\
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" # Forced float32 training\n"\
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" args.fp16 = False\n"\
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" args.bf16 = False\n"\
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" os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'\n"\
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"elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':\n"\
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" # Mixed precision training\n"\
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" args.fp16 = float16\n"\
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" args.bf16 = not float16\n"\
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" os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'\n"
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"elif mixed_precision_dtype == 'bfloat16':\n"\
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" # Both False since bfloat16 full finetuning doesn't do any autocasting.\n"\
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" args.fp16 = False\n"\
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" args.bf16 = False\n"\
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" os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'\n"
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@ -742,23 +742,15 @@ class FastBaseModel:
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torch.xpu.empty_cache()
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pass
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max_seq_length = model.max_seq_length
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# if we pass loftq_config = None we will get an error
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# If we pass loftq_config = None we will get an error
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loftq_config = validate_loftq_config(loftq_config, lora_dropout, bias, init_lora_weights, model)
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lora_config_dict = {
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"r" : r,
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"lora_alpha" : lora_alpha,
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"target_modules" : target_modules,
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"target_parameters" : kwargs.get("target_parameters", None),
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"lora_dropout" : lora_dropout,
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"bias" : bias,
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"task_type" : task_type,
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"modules_to_save" : modules_to_save,
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"use_rslora" : use_rslora,
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"init_lora_weights" : init_lora_weights,
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"loftq_config" : loftq_config,
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}
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# Get only allowed parameters for LoraConfig
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local_variables = { **locals(), **kwargs, }
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del local_variables["kwargs"]
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allowed_parameters = inspect.signature(LoraConfig).parameters.keys()
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lora_config = LoraConfig(
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**{k:v for k,v in lora_config_dict.items() if k in LoraConfig.__doc__},
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**{ k : v for k, v in local_variables.items() if k in allowed_parameters },
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)
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model = prepare_model_for_kbit_training(
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model,
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