Properly handle Params4bit in set_module_tensor_to_device #2934
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SunMarc
approved these changes
Jul 16, 2024
Comment on lines
+364
to
367
| if old_value.shape != value.shape and param_cls.__name__ != "Params4bit": | ||
| raise ValueError( | ||
| f'Trying to set a tensor of shape {value.shape} in "{tensor_name}" (which has shape {old_value.shape}), this look incorrect.' | ||
| f'Trying to set a tensor of shape {value.shape} in "{tensor_name}" (which has shape {old_value.shape}), this looks incorrect.' | ||
| ) |
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If I understood correctly here, the shape of the Param4bit is different from the actual weight that we are trying to set in the offload case. That happens because in with offload, the weight is not quantized.
Could you add a short comment to explain what happens here ?
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Yes, that's correct! The shape is changed and weights are packed (e.g. two nf4/fp4 values in uint8) with Params4bit. I will add a comment to explain that.
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@SunMarc cc @muellerzr @Titus-von-Koeller
I've added a comment to explain. Here's a small repro example for the shape mismatch.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
MODEL_ID = "facebook/opt-1.3b"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
quantization_config=quantization_config,
device_map="auto",
max_memory={0: "0.5GiB", "cpu": "8GiB"},
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
)
print(model, model.hf_device_map)
inputs = tokenizer("What is the meaning of life, the universe, and everything?", return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=16, num_return_sequences=1)
print(f"{tokenizer.decode(output[0])}")Output:
Some parameters are on the meta device device because they were offloaded to the cpu.
OPTForCausalLM(
(model): OPTModel(
(decoder): OPTDecoder(
(embed_tokens): Embedding(50272, 2048, padding_idx=1)
(embed_positions): OPTLearnedPositionalEmbedding(2050, 2048)
(final_layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
(layers): ModuleList(
(0-23): 24 x OPTDecoderLayer(
(self_attn): OPTAttention(
(k_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)
(v_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)
(q_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)
(out_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)
)
(activation_fn): ReLU()
(self_attn_layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
(fc1): Linear4bit(in_features=2048, out_features=8192, bias=True)
(fc2): Linear4bit(in_features=8192, out_features=2048, bias=True)
(final_layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
)
)
)
)
(lm_head): Linear(in_features=2048, out_features=50272, bias=False)
) {'model.decoder.embed_tokens': 0, 'lm_head': 0, 'model.decoder.embed_positions': 0, 'model.decoder.final_layer_norm': 0, 'model.decoder.layers.0': 0, 'model.decoder.layers.1': 0, 'model.decoder.layers.2': 0, 'model.decoder.layers.3': 0, 'model.decoder.layers.4': 0, 'model.decoder.layers.5': 0, 'model.decoder.layers.6': 0, 'model.decoder.layers.7': 0, 'model.decoder.layers.8': 0, 'model.decoder.layers.9': 'cpu', 'model.decoder.layers.10': 'cpu', 'model.decoder.layers.11': 'cpu', 'model.decoder.layers.12': 'cpu', 'model.decoder.layers.13': 'cpu', 'model.decoder.layers.14': 'cpu', 'model.decoder.layers.15': 'cpu', 'model.decoder.layers.16': 'cpu', 'model.decoder.layers.17': 'cpu', 'model.decoder.layers.18': 'cpu', 'model.decoder.layers.19': 'cpu', 'model.decoder.layers.20': 'cpu', 'model.decoder.layers.21': 'cpu', 'model.decoder.layers.22': 'cpu', 'model.decoder.layers.23': 'cpu'}
Traceback (most recent call last):
File "/home/matt/code/accelerate/./sandbox/inference.py", line 35, in <module>
output = model.generate(**inputs, max_new_tokens=16, num_return_sequences=1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 1914, in generate
result = self._sample(
^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 2651, in _sample
outputs = self(
^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/src/accelerate/hooks.py", line 169, in new_forward
output = module._old_forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/transformers/models/opt/modeling_opt.py", line 1118, in forward
outputs = self.model.decoder(
^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/transformers/models/opt/modeling_opt.py", line 884, in forward
layer_outputs = decoder_layer(
^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/src/accelerate/hooks.py", line 169, in new_forward
output = module._old_forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/transformers/models/opt/modeling_opt.py", line 525, in forward
hidden_states, self_attn_weights, present_key_value = self.self_attn(
^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/src/accelerate/hooks.py", line 169, in new_forward
output = module._old_forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/transformers/models/opt/modeling_opt.py", line 155, in forward
query_states = self.q_proj(hidden_states) * self.scaling
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/src/accelerate/hooks.py", line 164, in new_forward
args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/matt/code/accelerate/src/accelerate/hooks.py", line 354, in pre_forward
set_module_tensor_to_device(
File "/home/matt/code/accelerate/src/accelerate/utils/modeling.py", line 366, in set_module_tensor_to_device
raise ValueError(
ValueError: Trying to set a tensor of shape torch.Size([2048, 2048]) in "weight" (which has shape torch.Size([2097152, 1])), this look incorrect.
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Nice ! In a follow PR in transformers, I think we can finally remove the need for llm_int8_enable_fp32_cpu_offload in the 4bit case. In the 8bit case, this will still be required since we would still need to not convert the offloaded layers.
muellerzr
approved these changes
Jul 16, 2024
…_tensor_to_device
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What does this PR do?
This PR fixes compatibility for bitsandbytes
Params4bitandset_module_tensor_to_device.Related: bitsandbytes-foundation/bitsandbytes#1279
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@muellerzr @SunMarc