# compressed-tensors [compressed-tensors](https://github.com/neuralmagic/compressed-tensors) extends [safetensors](https://github.com/huggingface/safetensors) files to compressed tensor data types to provide a unified checkpoint format for storing and loading various quantization and sparsity formats such dense, int-quantized (int8), float-quantized (fp8), and pack-quantized (int4 or int8 weight-quantized packed into int32). compressed-tensors supports fine-tuning with [PEFT](https://huggingface.co/docs/peft) and includes the following features as well. - fp8, int4, int8 weight and activation precisions. - Quantization scales and zero-points strategies for [tensor, channel, group, block, token](https://github.com/neuralmagic/compressed-tensors/blob/83b2e7a969d70606421a76b9a3d112646077c8de/src/compressed_tensors/quantization/quant_args.py#L43-L52). - Dynamic per-token activation quantization (or any static strategy). - Weight sparsity (unstructured or semi-structured like 2:4) can be composed with quantization for extreme compression. - Quantization of arbitrary modules, not just [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) modules. - Targeted support for specific modules by name or class. Install compressed-tensors from [PyPI](https://pypi.org/project/compressed-tensors) to get the latest stable release (recommended) or install it from source to get the latest features. ```bash pip install compressed-tensors ``` ```bash git clone https://github.com/neuralmagic/compressed-tensors cd compressed-tensors pip install -e . ``` Search using the compressed-tensors [tag](https://huggingface.co/models?other=compressed-tensors) to find a compatible model on the Hugging Face Hub. Only models that have already been quantized can be loaded at the moment, and once a model is loaded, it cannot be saved. To quantize a model into the compressed-tensors format, see [llm-compressor](https://github.com/vllm-project/llm-compressor). Alternatively, models can be created independently and serizlied with a compressed-tensors config. ```python from transformers import AutoModelForCausalLM ct_model = AutoModelForCausalLM.from_pretrained("nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf", device_map="auto") # measure memory usage mem_params = sum([param.nelement()*param.element_size() for param in ct_model.parameters()]) print(f"{mem_params/2**30:.4f} GB") # 8.4575 GB ``` ## Model checkpoint compressed-tensor models are defined through its configuration entry. The following example is taken from the [nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf](https://huggingface.co/nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf/blob/main/config.json) `config.json` file. There are a lot of entries to allow for flexible expression both during and after compression, but the entries for loading and inference can be simplified to focus on just a few key entries. ```yaml "quantization_config": { "config_groups": { "group_0": { "input_activations": { "num_bits": 8, "strategy": "tensor", "type": "float" }, "targets": ["Linear"], "weights": { "num_bits": 8, "strategy": "tensor", "type": "float" } } }, "format": "naive-quantized", "ignore": ["lm_head"], "quant_method": "compressed-tensors", "quantization_status": "frozen" }, ``` The config file specifies the quantization of a config group (`group_0`), which includes weight and activation quantization to fp8 with a static per-tensor strategy. The `lm_head` module is unquantized as shown in the `ignore` key. For a more detailed look at the model weights, use the [safetensors viewer](https://huggingface.co/nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf?show_file_info=model.safetensors.index.json) on the model card to see the quantized weights, input scale, and weight scale for all [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) modules. | Tensors | Shape | Precision | | ------- | ----- | --------- | model.layers.0.input_layernorm.weight | [4 096] | BF16 model.layers.0.mlp.down_proj.input_scale | [1] | BF16 model.layers.0.mlp.down_proj.weight | [4 096, 14 336] | F8_E4M3 model.layers.0.mlp.down_proj.weight_scale | [1] | BF16 model.layers.0.mlp.gate_proj.input_scale | [1] | BF16 model.layers.0.mlp.gate_proj.weight | [14 336, 4 096] | F8_E4M3 model.layers.0.mlp.gate_proj.weight_scale | [1] | BF16 model.layers.0.mlp.up_proj.input_scale| [1] |BF16 model.layers.0.mlp.up_proj.weight | [14 336, 4 096] | F8_E4M3 model.layers.0.mlp.up_proj.weight_scale | [1] | BF16 model.layers.0.post_attention_layernorm.weight | [4 096] |BF16 model.layers.0.self_attn.k_proj.input_scale | [1] | BF16 model.layers.0.self_attn.k_proj.weight | [1 024, 4 096]| F8_E4M3 model.layers.0.self_attn.k_proj.weight_scale |[1] | BF16 model.layers.0.self_attn.o_proj.input_scale | [1] | BF16 model.layers.0.self_attn.o_proj.weight | [4 096, 4 096] | F8_E4M3 model.layers.0.self_attn.o_proj.weight_scale | [1] | BF16 model.layers.0.self_attn.q_proj.input_scale | [1] | BF16 model.layers.0.self_attn.q_proj.weight | [4 096, 4 096] | F8_E4M3 model.layers.0.self_attn.q_proj.weight_scale | [1] | BF16 model.layers.0.self_attn.v_proj.input_scale | [1] | BF16 model.layers.0.self_attn.v_proj.weight | [1 024, 4 096] | F8_E4M3 model.layers.0.self_attn.v_proj.weight_scale | [1] | BF16 When loading a compressed-tensors model with the [`~quantizers.HFQuantizer`] integration, all the [nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) modules specified in the quantization config are replaced by [CompressedLinear](https://github.com/neuralmagic/compressed-tensors/blob/975cb223b19fcac2b98a4271d17668462d4d6e1d/src/compressed_tensors/linear/compressed_linear.py#L30) modules that manage the compressed weights and forward pass for inference. The `lm_head` module is still kept as an unquantized nn.Linear module. ```python from transformers import AutoModelForCausalLM ct_model = AutoModelForCausalLM.from_pretrained("nm-testing/Meta-Llama-3.1-8B-Instruct-FP8-hf") print(ct_model) """ LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(128256, 4096) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaSdpaAttention( (q_proj): CompressedLinear( in_features=4096, out_features=4096, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (k_proj): CompressedLinear( in_features=4096, out_features=1024, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (v_proj): CompressedLinear( in_features=4096, out_features=1024, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (o_proj): CompressedLinear( in_features=4096, out_features=4096, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): CompressedLinear( in_features=4096, out_features=14336, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (up_proj): CompressedLinear( in_features=4096, out_features=14336, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (down_proj): CompressedLinear( in_features=14336, out_features=4096, bias=False (input_observer): MovingAverageMinMaxObserver() (weight_observer): MovingAverageMinMaxObserver() ) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05) (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05) ) ) (norm): LlamaRMSNorm((4096,), eps=1e-05) (rotary_emb): LlamaRotaryEmbedding() ) (lm_head): Linear(in_features=4096, out_features=128256, bias=False) ) """ ```