RuntimeError: Index put requires the source and destination dtypes match, got BFloat16 for the destination and Float for the source.
Usecase : I am trying to fine tune gemma2 using SFTTrainer and here is how I am loading the model and my bnb cofigs
model_params = {
"attn_implementation": "eager",
"torch_dtype": torch.bfloat16,
"use_cache": True,
"device_map": "auto",
}
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **model_params)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
BNB_CONFIG = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16
)
Training arguments
TRAINING_ARGS = {
"num_train_epochs": 1,
"optim": "adamw_torch_fused",
"logging_steps": 20,
"save_strategy": "epoch",
"bf16": True,
"tf32": True,
}
and when i try to use fine tuned model to generate predictions using this
outputs = model.generate(input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
temperature=temperature, pad_token_id=tokenizer.eos_token_id)
i am hitting this error, and the same script works fine with llama3, mistral, qwen ...
RuntimeError: Index put requires the source and destination dtypes match, got BFloat16 for the destination and Float for the source.
stacktrace :
outputs = model.generate(input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
File "/usr/local/lib/python3.10/dist-packages/peft/peft_model.py", line 1491, in generate
outputs = self.base_model.generate(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1914, in generate
result = self._sample(
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2651, in _sample
outputs = self(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py", line 166, in new_forward
output = module._old_forward(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 1068, in forward
outputs = self.model(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 908, in forward
layer_outputs = decoder_layer(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py", line 166, in new_forward
output = module._old_forward(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 650, in forward
hidden_states, self_attn_weights, present_key_value = self.self_attn(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py", line 166, in new_forward
output = module._old_forward(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 252, in forward
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/cache_utils.py", line 1071, in update
return update_fn(
File "/usr/local/lib/python3.10/dist-packages/transformers/cache_utils.py", line 1046, in _static_update
k_out[:, :, cache_position] = key_states
RuntimeError: Index put requires the source and destination dtypes match, got BFloat16 for the destination and Float for the source.
Hardware : NVIDIA H100 80GB
accelerate==0.31.0
bitsandbytes==0.43.1
datasets==2.18.0
deepspeed==0.14.4
evaluate==0.4.1
peft==0.11.1
transformers==4.42.3
trl==0.9.4
pytorch image : nvcr.io/nvidia/pytorch:24.05-py3 -- cuda 12.4.1 and torch 2.4
Do you know how to fix this bug?
But when i use model.to(torch.bfloat16) before generating predictions, I find this bug:
ValueError: .to is not supported for 4-bit or 8-bit bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct dtype.
+1
getting similar Error:RuntimeError: Index put requires the source and destination dtypes match, got Half for the destination and Float for the source.
sorry i did type same answer as above , so had to hide my above comment
Try to set use_cache to False; it has helped me.
+1
getting similar Error:RuntimeError: Index put requires the source and destination dtypes match, got Half for the destination and Float for the source.
Same issue for me here, using gemma2-2b and 4-bit quant.
Setting use_cache to False didn't solve the error, but setting model.to(torch.half) seems to correct it.