Upload BitLlamaForCausalLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "_name_or_path": "./myBit-Llama2-127M",
+  "architectures": [
+    "BitLlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "modeling_bit_llama.BitLlamaConfig",
+    "AutoModelForCausalLM": "modeling_bit_llama.BitLlamaForCausalLM"
+  },
+  "bitnet_type": "1.58b",
+  "bits": 8,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "max_position_embeddings": 1024,
+  "model_type": "bit_llama",
+  "n_ctx": 128,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.39.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3aba886075b96db2dd0e83d1dfa1e58a6b949a16b0cf6903580f363445e5ba6
+size 221157616

modeling_bit_llama.py

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+import warnings
+from typing import Optional, Tuple
+from transformers.models.llama.modeling_llama import (
+    LlamaConfig, 
+    LlamaModel,
+    LlamaForCausalLM,
+    LlamaAttention,
+    LlamaFlashAttention2,
+    LlamaSdpaAttention,
+    LlamaMLP,
+    LlamaDecoderLayer,
+)
+from mybitnet.bitnet import BitLinear, BitLinear158b
+import torch
+from torch import nn
+
+class BitLlamaConfig(LlamaConfig):
+    model_type = "bit_llama"
+
+    def __init__(self, bitnet_type="1.58b", bits=8, **kwargs):
+        super().__init__(**kwargs)
+        self.bitnet_type = bitnet_type
+        if self.bitnet_type not in ["1.58b", "1b"]:
+            raise ValueError("bitnet_type must be either '1.58b' or '1b'.")
+        self.bits = bits
+
+
+class BitLlamaMLP(LlamaMLP):
+    def __init__(self, config):
+        super().__init__(config)
+        if config.bitnet_type=="1b":
+            self.gate_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=False)
+            self.up_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.down_proj = BitLinear(self.intermediate_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+        elif config.bitnet_type=="1.58b":
+            self.gate_proj = BitLinear158b(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.up_proj = BitLinear158b(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.down_proj = BitLinear158b(self.intermediate_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+        else:
+            raise ValueError("bitnet_type must be either '1.58b' or '1b'.")
+        
+class BitLlamaAttention(LlamaAttention):
+    def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
+        super().__init__(config)
+        if config.bitnet_type=="1b":
+            self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+        elif config.bitnet_type=="1.58b":
+            self.q_proj = BitLinear158b(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.k_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.v_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.o_proj = BitLinear158b(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+        else:
+            raise ValueError("bitnet_type must be either '1.58b' or '1b'.")
+
+class BitLlamaFlashAttention2(LlamaFlashAttention2):
+    def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        if config.bitnet_type=="1b":
+            self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+        elif config.bitnet_type=="1.58b":
+            self.q_proj = BitLinear158b(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.k_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.v_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.o_proj = BitLinear158b(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+        else:
+            raise ValueError("bitnet_type must be either '1.58b' or '1b'.")
+
+class BitLlamaSdpaAttention(LlamaSdpaAttention):
+    def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        if config.bitnet_type=="1b":
+            self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+            self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True)
+        elif config.bitnet_type=="1.58b":
+            self.q_proj = BitLinear158b(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.k_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.v_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+            self.o_proj = BitLinear158b(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits)
+        else:
+            raise ValueError("bitnet_type must be either '1.58b' or '1b'.")
+
+BITLLAMA_ATTENTION_CLASSES = {
+    "eager": BitLlamaAttention,
+    "flash_attention_2": BitLlamaFlashAttention2,
+    "sdpa": BitLlamaSdpaAttention,
+}
+
+class BitLlamaDecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: BitLlamaConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.self_attn = BITLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+        self.mlp = BitLlamaMLP(config)
+        del self.input_layernorm
+        del self.post_attention_layernorm
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        refers: https://github.com/huggingface/transformers/blob/c5f0288bc7d76f65996586f79f69fba8867a0e67/src/transformers/models/llama/modeling_llama.py#L693
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+class BitLlamaModel(LlamaModel):
+    config_class = BitLlamaConfig
+
+    def __init__(self, config: BitLlamaConfig):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [BitLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+class BitLlamaForCausalLM(LlamaForCausalLM):
+    config_class = BitLlamaConfig
+
+    def __init__(self, config: BitLlamaConfig):
+        super().__init__(config)
+        self.model = BitLlamaModel(config)
+        self.lm_head = BitLinear(config.hidden_size, config.vocab_size, bias=False, bits=config.bits, flg_before_linear=True)
+