feat: merge dcu branch features

examples/bitnet-1.58b/benchmark_generate.py

+import torch
+import bitblas
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+from transformers import GenerationConfig
+import time
+import argparse
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+
+def generate_text_batch(model, tokenizer, prompts, max_length=100):
+    # Encode the input prompts as a batch
+    input_ids = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True).input_ids.to(model.device)
+
+    # Generate cos and sin values (commented out as not used in generation)
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+    # position_embeddings = model.embed_positions(position_ids)
+    # cos = position_embeddings[:, :, 0::2].cos()
+    # sin = position_embeddings[:, :, 1::2].sin()
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    # output_ids = model.generate(input_ids, generation_config=generation_config, cos=cos, sin=sin)
+    end_time = time.time()
+
+    # Decode the output ids to text
+    generated_texts = [tokenizer.decode(output_id, skip_special_tokens=True) for output_id in output_ids]
+
+    generation_time = end_time - start_time
+    num_tokens = sum(len(output_id) for output_id in output_ids)
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_texts
+
+
+def profile(model, input_data):
+    import numpy as np
+
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+model_path = "1bitLLM/bitnet_b1_58-3B"
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--bs", default=16, type=int)
+    parser.add_argument("--in_seq_len", default=32, type=int)
+    parser.add_argument("--out_seq_len", default=128, type=int)
+    parser.add_argument("--bitblas", action="store_true")
+    args = parser.parse_args()
+    bs = args.bs
+    in_seq_len = args.in_seq_len
+    out_seq_len = args.out_seq_len
+    is_bitblas = args.bitblas
+    model = (
+        BitnetForCausalLM.from_pretrained(
+            model_path,
+            use_flash_attention_2=True,
+            torch_dtype=torch.float16,
+        )
+        .cuda()
+        .half()
+    )
+    if is_bitblas:
+        with torch.no_grad():
+            model.quantize()
+
+    tokenizer = BitnetTokenizer.from_pretrained(model_path)
+    prompt = ""
+    for _ in range(in_seq_len):
+        prompt += "Hello "
+
+    prompts = []
+    for _ in range(bs):
+        prompts.append(prompt)
+    max_length = out_seq_len + in_seq_len
+    print(generate_text_batch(model, tokenizer, prompts, max_length=max_length))
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/benchmark_inference_latency.py

+import argparse
+import torch
+
+from modeling_bitnet import BitnetForCausalLM
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str)
+
+
+def profile(model, input_data):
+    import time
+
+    import numpy as np
+
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        "1bitLLM/bitnet_b1_58-3B",
+        device_map="auto",
+        low_cpu_mem_usage=True,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    with torch.no_grad():
+        model.quantize()
+        model = torch.compile(model)
+
+    benchmark_sets = [(1024, 1), (1, 2048)]
+    for batch_size, seq_len in benchmark_sets:
+        input_id = torch.ones(batch_size, seq_len).long().cuda()
+        latency = profile(model, input_id)
+        print(f"Batch size: {batch_size}, Seq len: {seq_len}, Latency: {latency}")
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/benchmark_model_10k_loops.py

+import argparse
+import torch
+
+from modeling_bitnet import BitnetForCausalLM
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str)
+parser.add_argument("--batch_size", default=1, type=int)
+parser.add_argument("--seq_len", default=1, type=int)
+
+args = parser.parse_args()
+
+seq_len = args.seq_len
+batch_size = args.batch_size
+
+
+def profile(model, input_data):
+    import time
+
+    import numpy as np
+
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        "1bitLLM/bitnet_b1_58-3B",
+        device_map="auto",
+        low_cpu_mem_usage=True,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    with torch.no_grad():
+        model._post_process_weights()
+
+    torch.cuda.empty_cache()
+
+    input_id = torch.ones(batch_size, seq_len).long().cuda()
+    for _ in range(10000):
+        _ = model(input_id)
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/configuration_bitnet.py

+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class BitnetConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BitnetModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BitnetModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Bitnet 1 supports up to 2048 tokens,
+            Bitnet 2 up to 4096, CodeBitnet up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import BitnetModel, BitnetConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = BitnetConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = BitnetModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        weight_bits=1,
+        input_bits=8,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.weight_bits = weight_bits
+        self.input_bits = input_bits
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(f"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got {self.rope_scaling}")
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

examples/bitnet-1.58b/eval_correctness.py

+import torch
+import bitblas
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+from transformers import GenerationConfig
+import time
+import transformers
+
+print(f"transformers version is {transformers.__version__}")
+
+# version must be lower than or equal to 4.40
+assert transformers.__version__ <= "4.40.0"
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+
+def generate_text(model, tokenizer, prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.lm_head.weight.device)
+    # Generate cos and sin values
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    end_time = time.time()
+
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    generation_time = end_time - start_time
+    num_tokens = len(output_ids[0])
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_text
+
+
+def profile(model, input_data):
+    import numpy as np
+
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+model_path = "1bitLLM/bitnet_b1_58-3B"
+
+
+def main():
+    model = (
+        BitnetForCausalLM.from_pretrained(
+            model_path,
+            use_flash_attention_2=False,
+            torch_dtype=torch.float16,
+        )
+        .cuda()
+        .half()
+    )
+
+    tokenizer = BitnetTokenizer.from_pretrained(model_path, use_fast=False)
+    input_id = tokenizer("Hello")["input_ids"]
+    input_id = torch.tensor(input_id).unsqueeze(0).cuda()
+
+    print("original model generated text:")
+    print(generate_text(model, tokenizer, "Hello", max_length=100))
+
+    model.quantize()
+    print("quantized model generated text:")
+    print(generate_text(model, tokenizer, "Hello", max_length=100))
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/eval_gpu_memory.py

+import argparse
+import torch
+
+from modeling_bitnet import BitnetForCausalLM
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str)
+
+
+def profile(model, input_data):
+    import time
+
+    import numpy as np
+
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        "1bitLLM/bitnet_b1_58-3B",
+        device_map="auto",
+        low_cpu_mem_usage=True,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    print(f"gpu memory: {torch.cuda.memory_allocated() / 1024**3} GB")
+    with torch.no_grad():
+        model._post_process_weights()
+    print(f"gpu memory BitBLAS: {torch.cuda.memory_allocated() / 1024**3} GB")
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/eval_ppl.py

+# pylint: disable=missing-docstring, invalid-name
+"""This is modified from https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/main/utils_quant.py."""
+
+import math
+import argparse
+import torch
+import random
+
+from eval_utils import get_test_dataset
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+
+from tqdm import tqdm
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--seed", default=0, type=int)
+parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str)
+parser.add_argument("--seqlen", default=2048, type=int)
+
+
+def calulate_loss(model, input, loss_fct):
+    output = model(input, use_cache=False, output_hidden_states=False, output_attentions=False)[0]
+    shift_logits = output[:, :-1, :].contiguous()
+    shift_labels = input[:, 1:]
+    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+    return loss
+
+
+def main(args):
+    datasets = ["c4", "wikitext2"]
+    model = (
+        BitnetForCausalLM.from_pretrained(
+            args.hf_path,
+            use_flash_attention_2=True,
+            torch_dtype=torch.float16,
+        )
+        .cuda()
+        .half()
+    )
+    with torch.no_grad():
+        model._post_process_weights()
+    tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False)
+    loss_fct = torch.nn.CrossEntropyLoss(reduction="sum").cuda()
+
+    ppl = []
+    for dataset in datasets:
+        testdata = get_test_dataset(dataset, tokenizer, seqlen=args.seqlen)
+        acc_loss, count = 0.0, 0
+        progress = tqdm(range(len(testdata)))
+        for ii in progress:
+            input = torch.Tensor(testdata[ii]).long().cuda().view(1, -1)
+            loss = calulate_loss(model, input, loss_fct)
+            count += input.size(-1) - 1
+            acc_loss += loss.item()
+            progress.set_description(f"avg_loss = {acc_loss / count / math.log(2)}")
+
+        avg_loss = acc_loss / count / math.log(2)
+        ppl.append(2**avg_loss)
+        print("{} PPL: {}".format(dataset, ppl[-1]))
+
+    print(ppl)
+    print("Avg PPL:", sum(ppl) / len(ppl))
+
+
+if __name__ == "__main__":
+    torch.set_grad_enabled(False)
+    args = parser.parse_args()
+    random.seed(args.seed)
+    torch.random.manual_seed(args.seed)
+    main(args)

examples/bitnet-1.58b/eval_utils.py

+# ruff: noqa
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+from lm_eval.base import BaseLM
+from datasets import load_dataset
+
+
+def set_seed(seed):
+    np.random.seed(seed)
+    torch.random.manual_seed(seed)
+
+
+def get_test_dataset(dataset_name, tokenizer, seqlen=2048):
+    if dataset_name == "wikitext2":
+        testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
+        testdata = "".join(testdata["text"]).split("\n")
+    elif dataset_name == "c4":
+        testdata = load_dataset("allenai/c4", data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"}, split="validation")[
+            "text"
+        ]
+    else:
+        raise NotImplementedError
+
+    testdata = [item for item in testdata if item != ""]
+    tokenized_text = [tokenizer(item, add_special_tokens=False)["input_ids"] + [tokenizer.eos_token_id] for item in testdata]
+
+    data, doc = [], [tokenizer.bos_token_id]
+    for sen in tokenized_text:
+        if len(sen) > seqlen:
+            continue
+        if len(doc) + len(sen) > seqlen:
+            data.append(doc)
+            doc = [tokenizer.bos_token_id]
+        doc.extend(sen)
+    if len(doc) > 1 and len(doc) <= seqlen:
+        data.append(doc)
+    return data
+
+
+class LMEvalAdaptor(BaseLM):
+    def __init__(self, model_name, model, tokenizer, batch_size=1, max_length=-1):
+        super().__init__()
+
+        assert isinstance(batch_size, int)
+
+        self.model_name = model_name
+        self.model = model
+        self.model.eval()
+
+        self.tokenizer = tokenizer
+
+        self.vocab_size = self.tokenizer.vocab_size
+
+        self._batch_size = batch_size
+
+        self._max_length = max_length
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        if self._max_length != -1:
+            return self._max_length
+        if hasattr(self.model.config, "n_ctx"):
+            return self.model.config.n_ctx
+        elif hasattr(self.model.config, "max_position_embeddings"):
+            return self.model.config.max_position_embeddings
+        elif hasattr(self.model.config, "n_positions"):
+            return self.model.config.n_positions
+        elif "bloom" in self.model_name:
+            return 2048
+        elif "llama" in self.model_name:
+            return 2048  # TODO: did not check this
+        elif "mpt" in self.model_name:
+            return 2048
+        elif "falcon" in self.model_name:
+            return 2048
+        else:
+            print(self.model.config)
+            raise NotImplementedError
+
+    @property
+    def max_gen_toks(self):
+        return 256
+
+    @property
+    def batch_size(self):
+        return self._batch_size
+
+    @property
+    def device(self):
+        return "cuda"
+
+    def tok_encode(self, string: str, add_special_tokens=True):
+        return self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+
+    def tok_decode(self, tokens):
+        return self.tokenizer.decode(tokens)
+
+    def loglikelihood(self, requests):
+        new_reqs = []
+        for context, continuation in requests:
+            context, continuation = context.strip(), continuation.strip()
+            if context == "":
+                # end of text as context
+                context_enc = [self.eot_token_id]
+            else:
+                context_enc = self.tok_encode(context, add_special_tokens=True)
+
+            continuation_enc = self.tok_encode(continuation, add_special_tokens=False)
+
+            new_reqs.append(((context, continuation), context_enc, continuation_enc))
+
+        return self._loglikelihood_tokens(new_reqs)
+
+    def _model_call(self, inps):
+        """
+        inps: a torch tensor of shape [batch, sequence]
+        the size of sequence may vary from call to call
+
+        returns: a torch tensor of shape [batch, sequence, vocab] with the
+        logits returned from the model
+        """
+        with torch.no_grad():
+            out = self.model(inps)[0]
+        return out
+
+    def _model_generate(self, context, max_length, eos_token_id):
+        return self.model.generate(context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False)

examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_decode.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch
+import torch.backends
+import tilelang
+import tilelang.language as T
+from tilelang import tvm as tvm
+from tvm import DataType
+import numpy as np
+
+from tilelang.transform import simplify_prim_func
+
+torch.manual_seed(42)
+
+decode_i2s_to_i8s = """template <typename T1, typename T2>
+__device__ void decode_i2s_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+    static constexpr uint MEDIAN_NUM = 0x02020202;
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+        i8s[i] = __vsub4(i8s[i], MEDIAN_NUM);
+    }
+}
+template <typename T1, typename T2>
+__device__ void decode_i2u_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+    }
+}
+"""
+
+
+@simplify_prim_func
+def bitnet_158_int8xint2_decode(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+    fast_decoding=True,
+    n_partition=4,
+    reduce_thread=32,
+):
+    assert in_dtype in [
+        T.float16,
+        T.int8,
+    ], "Currently only float16 and int8 are supported"
+    assert out_dtype in [
+        T.float16,
+        T.float32,
+        T.int32,
+    ], "Currently only float16, float32 and int32 are supported"
+    storage_nbit = 8
+    num_bits = 2
+    A_shape = (M, K)
+    B_shape = (N, K // storage_nbit * num_bits)
+    C_shape = (M, N)
+
+    num_elems_per_byte = 4
+    MAX_TRANSACTION_SIZE_IN_BITS = 128
+    micro_size_k = MAX_TRANSACTION_SIZE_IN_BITS // DataType(in_dtype).bits
+    micro_size_k_compressed = micro_size_k // num_elems_per_byte
+    storage_dtype = T.int8
+    block_K = reduce_thread * micro_size_k
+
+    use_dp4a = True
+    dp4a_size = 4
+
+    @T.prim_func
+    def kernel(
+        A: T.Buffer(A_shape, in_dtype),
+        B: T.Buffer(B_shape, storage_dtype),
+        C: T.Buffer(C_shape, out_dtype),
+    ):
+        with T.Kernel(
+            T.ceildiv(N, n_partition),
+            M,
+            threads=(reduce_thread, n_partition),
+        ) as (
+            bx,
+            by,
+        ):
+            A_local = T.alloc_local((micro_size_k,), in_dtype)
+            B_quant_local = T.alloc_local([micro_size_k_compressed], storage_dtype)
+            B_dequantize_local = T.alloc_local([micro_size_k], in_dtype)
+            accum_res = T.alloc_local((1,), accum_dtype)
+            reduced_accum_res = T.alloc_local((1,), accum_dtype)
+
+            kr = T.thread_binding(0, reduce_thread, thread="threadIdx.x")
+            ni = T.thread_binding(0, n_partition, thread="threadIdx.y")
+
+            T.import_source(decode_i2s_to_i8s)
+
+            T.clear(accum_res)
+            for ko in T.serial(T.ceildiv(K, block_K)):
+                for v in T.vectorized(micro_size_k):
+                    A_local[v] = A[by, ko * block_K + kr * micro_size_k + v]
+
+                for v in T.vectorized(micro_size_k_compressed):
+                    B_quant_local[v] = B[
+                        bx * n_partition + ni,
+                        ko * (reduce_thread * micro_size_k_compressed) + kr * micro_size_k_compressed + v,
+                    ]
+
+                T.call_extern(
+                    "handle",
+                    "decode_i2u_to_i8s",
+                    T.address_of(B_quant_local[0]),
+                    T.address_of(B_dequantize_local[0]),
+                )
+
+                if use_dp4a:
+                    for ki in T.serial(micro_size_k // dp4a_size):
+                        T.dp4a(
+                            A_local[ki * dp4a_size],
+                            B_dequantize_local[ki * dp4a_size],
+                            accum_res[0],
+                        )
+                else:
+                    for ki in T.serial(micro_size_k):
+                        accum_res[0] += A_local[ki] * B_dequantize_local[ki]
+
+            with T.attr(
+                T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]),
+                "reduce_scope",
+                T.reinterpret(T.uint64(0), dtype="handle"),
+            ):
+                T.evaluate(
+                    T.tvm_thread_allreduce(
+                        T.uint32(1),
+                        accum_res[0],
+                        True,
+                        reduced_accum_res[0],
+                        kr,
+                        dtype="handle",
+                    )
+                )
+            if kr == 0:
+                C[by, bx * n_partition + ni] = reduced_accum_res[0]
+
+    return kernel
+
+
+def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8):
+    elems_per_byte = 8 // source_bits
+    if lowprecision_weight.dtype == np.float16:
+        lowprecision_weight = lowprecision_weight.astype(dtype=np.int8)
+    int8_weight = np.zeros(
+        (
+            *lowprecision_weight.shape[:-1],
+            lowprecision_weight.shape[-1] // elems_per_byte,
+        ),
+        dtype=np.int8,
+    )
+    for j in range(lowprecision_weight.shape[-1] // elems_per_byte):
+        for k in range(elems_per_byte):
+            int8_weight[:, j] |= lowprecision_weight[:, j * elems_per_byte + k] << (source_bits * k)
+
+    return int8_weight.view(storage_dtype)
+
+
+# interleave weight numpy implementation
+def interleave_weight(qweight, nbits=4, target_dtype=T.float16):
+    assert target_dtype in [T.float16, T.int8]
+    # reinterpret the data type of qweight to int32
+    qweight = qweight.view(np.int32)
+    new_qweight = np.zeros_like(qweight)
+    bits_stride = 8 if target_dtype == T.int8 else 16
+    mask = (1 << nbits) - 1  # for 4bit the val is 0x0000000f
+    num_groups = 32 // bits_stride
+    elems_per_group = bits_stride // nbits
+    for i in range(num_groups):
+        for j in range(elems_per_group):
+            offset = i * elems_per_group + j
+            shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits
+            new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift
+
+    if nbits == 1 and target_dtype == T.int8:
+        # special handling for 1b interleave
+        n16_weight = new_qweight & np.int32(0xF0F00F0F)
+        n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16
+        n16_weight |= ((new_qweight & np.int32(0x0000F000)) >> 12) << 24
+        n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4
+        n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12
+        return n16_weight.view(np.int8)
+    elif nbits == 2 and target_dtype == T.float16:
+        n8_weight = new_qweight & np.int32(0xFF0000FF)
+        n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16
+        n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8
+        return n8_weight.view(np.int8)
+    elif nbits == 1 and target_dtype == T.float16:
+        n8_weight = new_qweight & 0xF000000F
+        n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8
+        n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16
+        n8_weight |= ((new_qweight & 0x0000F000) >> 12) << 24
+        n8_weight |= ((new_qweight & 0x000F0000) >> 16) << 4
+        n8_weight |= ((new_qweight & 0x00F00000) >> 20) << 12
+        n8_weight |= ((new_qweight & 0x0F000000) >> 24) << 20
+
+    return new_qweight.view(np.int8)
+
+
+def assert_bitnet_158_int8xint2_decode_correctness(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding=True):
+    program = bitnet_158_int8xint2_decode(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding)
+    print(program)
+    kernel = tilelang.compile(program)
+    src_code = kernel.get_kernel_source()
+    # src_code is the generated cuda source
+    assert src_code is not None
+    print(src_code)
+    A = torch.randint(0, 4, (M, K), device="cuda", dtype=getattr(torch, in_dtype))
+    B = torch.randint(0, 2, (N, K), device="cuda", dtype=getattr(torch, in_dtype))
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    qw = general_compress(B.cpu().numpy(), source_bits=2, storage_dtype=np.int8)
+    qw = interleave_weight(qw, 2, target_dtype=in_dtype)
+    qw = torch.from_numpy(qw).to(device="cuda")
+
+    kernel(A, qw, C)
+    # Get Reference Result
+    ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, accum_dtype))
+
+    print(ref_c)
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+    assert_bitnet_158_int8xint2_decode_correctness(1, 256, 256, T.int8, T.int32, T.int32)

examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch
+import torch.backends
+import tilelang
+import tilelang.language as T
+from tilelang import tvm as tvm
+from tvm import DataType
+from tilelang.intrinsics.mma_layout import (
+    make_mma_swizzle_layout as make_swizzle_layout,
+)
+import numpy as np
+
+from tilelang.intrinsics.mma_macro_generator import (
+    INT4TensorCoreIntrinEmitter,
+)
+from tilelang.transform import simplify_prim_func
+
+torch.manual_seed(42)
+
+decode_i2s_to_i8s = """template <typename T1, typename T2>
+__device__ void decode_i2s_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+    static constexpr uint MEDIAN_NUM = 0x02020202;
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+        i8s[i] = __vsub4(i8s[i], MEDIAN_NUM);
+    }
+}
+template <typename T1, typename T2>
+__device__ void decode_i2u_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+    }
+}
+"""
+
+
+@simplify_prim_func
+def bitnet_158_int8xint2_prefill(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+    fast_decoding=True,
+    block_row_warps=2,
+    block_col_warps=2,
+    warp_row_tiles=32,
+    warp_col_tiles=32,
+    chunk=64,
+):
+    """
+    Create a TVM GPU prim_func implementing a block-tiled matrix multiply that multiplies dense A by compressed/interleaved low‑precision B (2-bit packed into int8 storage), decoding B to int8 on-chip and accumulating into C.
+
+    The returned prim_func expects:
+    - A: shape (M, K) with dtype `in_dtype` (T.float16 or T.int8).
+    - B: compressed storage with shape (N, K/4) and int8 storage layout (packing 4 2-bit elements per byte).
+    - C: output buffer shape (M, N) with dtype `out_dtype` (T.float16, T.float32, or T.int32).
+
+    Details:
+    - Builds a tiled, pipelined kernel using shared memory and warp-level MMA intrinsics (INT4TensorCoreIntrinEmitter). B is loaded from compressed storage, decoded to int8 in threads (via decode_i2u_to_i8s / decode_i2s_to_i8s), and dequantized into a shared buffer used by the MMA emitter.
+    - Tiling parameters:
+      - block_row_warps, block_col_warps: number of warps per block in row/col.
+      - warp_row_tiles, warp_col_tiles: tiles per warp.
+      - chunk: K-sized chunk per block (block_K).
+      - micro sizes are fixed (16x16x16, except micro_k=32 when accum_dtype == T.int32).
+    - Uses 2-stage pipelining by default to overlap loads and compute and applies a swizzle layout to improve L2 behavior.
+    - Assertions: raises AssertionError if in_dtype or out_dtype are not among supported values.
+
+    Parameters:
+        M, N, K (int): Global matrix dimensions.
+        in_dtype (str): Input and decoded B element dtype; T.float16 or T.int8.
+        out_dtype (str): Output C dtype; one of T.float16, T.float32, T.int32.
+        accum_dtype (str): Accumulator dtype used by MMA (e.g., T.int32).
+        fast_decoding (bool): If True, enable the fast decoding path (affects which device decode is used).
+        block_row_warps (int): Warps in block row dimension.
+        block_col_warps (int): Warps in block column dimension.
+        warp_row_tiles (int): Tiles per warp in row dimension.
+        warp_col_tiles (int): Tiles per warp in column dimension.
+        chunk (int): K-length per block (block_K).
+
+    Returns:
+        T.prim_func: A TVM prim_func implementing the described GPU kernel suitable for compilation and execution.
+    """
+    assert in_dtype in [
+        T.float16,
+        T.int8,
+    ], "Currently only float16 and int8 are supported"
+    assert out_dtype in [
+        T.float16,
+        T.float32,
+        T.int32,
+    ], "Currently only float16, float32 and int32 are supported"
+
+    micro_size_x = micro_size_y = micro_size_k = 16
+
+    if accum_dtype == T.int32:
+        micro_size_k = 32
+
+    num_elems_per_byte = 4
+    MAX_TRANSACTION_SIZE_IN_BITS = 128
+    local_size = MAX_TRANSACTION_SIZE_IN_BITS // DataType(in_dtype).bits
+    local_size_compressed = local_size // num_elems_per_byte
+
+    shared_scope = "shared.dyn"
+    storage_dtype = T.int8
+
+    # Pipeline Stage
+    stage = 2
+
+    block_M = block_row_warps * warp_row_tiles
+    block_N = block_col_warps * warp_col_tiles
+    block_K = chunk
+
+    A_shape = (M, K)  # int8 storage represents int4*2
+    B_shape = (N, K // num_elems_per_byte)  # int8 storage represents int4*2
+    A_shared_shape = (block_M, block_K)
+    B_shared_shape = (block_N, block_K // num_elems_per_byte)
+    B_dequantize_shared_shape = (block_N, block_K)
+    C_shared_shape = (
+        block_M // micro_size_x,
+        block_N // micro_size_y,
+        micro_size_x,
+        micro_size_y,
+    )
+
+    warp_size = 32
+    threads = warp_size * (block_row_warps * block_col_warps)
+    fragement_size_a = (micro_size_x * micro_size_k) // warp_size
+    fragement_size_b = (micro_size_y * micro_size_k) // warp_size
+    fragement_size_c = (micro_size_x * micro_size_y) // warp_size
+    warp_rows = warp_row_tiles // micro_size_x
+    warp_cols = warp_col_tiles // micro_size_y
+
+    # MMA Wrapper to Auto Generate Code for MMA
+    mma_emitter = INT4TensorCoreIntrinEmitter(
+        a_dtype=in_dtype,
+        b_dtype=in_dtype,
+        accum_dtype=accum_dtype,
+        a_transposed=False,
+        b_transposed=True,
+        block_row_warps=block_row_warps,
+        block_col_warps=block_col_warps,
+        warp_row_tiles=warp_row_tiles,
+        warp_col_tiles=warp_col_tiles,
+        chunk=chunk,
+    )
+
+    @T.prim_func
+    def main(
+        A: T.Buffer(A_shape, in_dtype),
+        B: T.Buffer(B_shape, storage_dtype),
+        C: T.Buffer((M, N), out_dtype),
+    ):
+        """
+        GPU kernel entry that performs a blocked, pipelined matrix multiplication A @ B.T writing into C.
+
+        This kernel:
+        - Loads tiles of A and a compressed/interleaved representation of B from global memory into shared memory.
+        - Decodes B's packed low-precision format (storage_dtype, e.g., 2-bit packed) into element values of `in_dtype` in shared memory via an external decode routine.
+        - Uses Warp/MMA tiled fragments and an INT4/INT2-capable MMA emitter to compute accumulation across K in a pipelined fashion with configurable stages.
+        - Writes accumulated tile results from shared memory back to global C with the expected block/micro-tile indexing.
+
+        Parameters:
+            A: Input matrix buffer of shape A_shape and element type `in_dtype`. Represents the MxK activations.
+            B: Compressed/interleaved weight buffer of shape B_shape and storage type `storage_dtype`. Must contain B in the packed low-precision layout expected by the decode routine used by this kernel.
+            C: Output buffer of shape (M, N) and type `out_dtype`; receives the resulting matrix (accumulated values are produced in `accum_dtype` and stored into C).
+
+        Side effects:
+            Writes results into C. Calls external device decode functions to expand B from its packed representation into shared memory before computation.
+        """
+        with T.Kernel(
+            T.ceildiv(N, block_N),
+            T.ceildiv(M, block_M),
+            threads=threads,
+            prelude=decode_i2s_to_i8s,
+        ) as (bx, by):
+            A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, storage_dtype, scope=shared_scope)
+            B_dequantize_shared = T.alloc_shared(B_dequantize_shared_shape, in_dtype, scope=shared_scope)
+            C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
+            A_frag = T.alloc_local((warp_rows * fragement_size_a), in_dtype)
+            B_frag = T.alloc_local((warp_cols * fragement_size_b), in_dtype)
+            C_frag = T.alloc_local((warp_rows * warp_cols * fragement_size_c), accum_dtype)
+
+            B_local = T.alloc_local([local_size_compressed], storage_dtype)
+            B_dequantize_local = T.alloc_local([local_size], in_dtype)
+
+            thread_bindings = T.thread_binding(0, threads, "threadIdx.x")
+
+            T.annotate_layout(
+                {
+                    A_shared: make_swizzle_layout(A_shared),
+                    B_dequantize_shared: make_swizzle_layout(B_dequantize_shared),
+                }
+            )
+
+            # Improve L2 Cache
+            T.use_swizzle(panel_size=10)
+
+            T.clear(C_frag)
+
+            for ko in T.Pipelined((K // block_K), num_stages=stage):
+                # Load A into shared memory
+                for i, k in T.Parallel(block_M, block_K):
+                    A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
+
+                # Load B into shared memory
+                for j, k in T.Parallel(block_N, block_K // num_elems_per_byte):
+                    B_shared[j, k] = B[bx * block_N + j, ko * (block_K // num_elems_per_byte) + k]
+
+                for i in T.serial(block_N * block_K // num_elems_per_byte // (threads * local_size_compressed)):
+                    for v in T.vectorized(0, local_size_compressed):
+                        index = i * threads * local_size_compressed + thread_bindings * local_size_compressed + v
+                        vi, vj = T.index_to_coordinates(index, B_shared_shape)
+                        B_local[v] = B_shared[vi, vj]
+
+                    T.call_extern(
+                        "handle",
+                        "decode_i2u_to_i8s",
+                        T.address_of(B_local[0]),
+                        T.address_of(B_dequantize_local[0]),
+                    )
+
+                    for v in T.vectorized(0, local_size):
+                        index = i * threads * local_size + thread_bindings * local_size + v
+                        vi, vj = T.index_to_coordinates(index, B_dequantize_shared_shape)
+                        B_dequantize_shared[vi, vj] = B_dequantize_local[v]
+
+                for ki in T.serial(0, (block_K // micro_size_k)):
+                    # Load A into fragment
+                    mma_emitter.ldmatrix_a(
+                        A_frag,
+                        A_shared,
+                        ki,
+                    )
+
+                    # Load B into fragment
+                    mma_emitter.ldmatrix_b(
+                        B_frag,
+                        B_dequantize_shared,
+                        ki,
+                    )
+
+                    # Perform Matrix Multiplication
+                    mma_emitter.mma(A_frag, B_frag, C_frag)
+
+            # Perform STMatrix
+            mma_emitter.stmatrix(
+                C_frag,
+                C_shared,
+            )
+
+            # Store shared into global
+            for i, j in T.Parallel(block_M, block_N):
+                C[by * block_M + i, bx * block_N + j] = C_shared[
+                    i // micro_size_x,
+                    j // micro_size_y,
+                    i % micro_size_x,
+                    j % micro_size_y,
+                ]
+
+    return main
+
+
+def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8):
+    elems_per_byte = 8 // source_bits
+    if lowprecision_weight.dtype == np.float16:
+        lowprecision_weight = lowprecision_weight.astype(dtype=np.int8)
+    int8_weight = np.zeros(
+        (
+            *lowprecision_weight.shape[:-1],
+            lowprecision_weight.shape[-1] // elems_per_byte,
+        ),
+        dtype=np.int8,
+    )
+    for j in range(lowprecision_weight.shape[-1] // elems_per_byte):
+        for k in range(elems_per_byte):
+            int8_weight[:, j] |= lowprecision_weight[:, j * elems_per_byte + k] << (source_bits * k)
+
+    return int8_weight.view(storage_dtype)
+
+
+# interleave weight numpy implementation
+def interleave_weight(qweight, nbits=4, target_dtype=T.float16):
+    assert target_dtype in [T.float16, T.int8]
+    # reinterpret the data type of qweight to int32
+    qweight = qweight.view(np.int32)
+    new_qweight = np.zeros_like(qweight)
+    bits_stride = 8 if target_dtype == T.int8 else 16
+    mask = (1 << nbits) - 1  # for 4bit the val is 0x0000000f
+    num_groups = 32 // bits_stride
+    elems_per_group = bits_stride // nbits
+    for i in range(num_groups):
+        for j in range(elems_per_group):
+            offset = i * elems_per_group + j
+            shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits
+            new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift
+
+    if nbits == 1 and target_dtype == T.int8:
+        # special handling for 1b interleave
+        n16_weight = new_qweight & np.int32(0xF0F00F0F)
+        n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16
+        n16_weight |= ((new_qweight & np.int32(0x0000F000)) >> 12) << 24
+        n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4
+        n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12
+        return n16_weight.view(np.int8)
+    elif nbits == 2 and target_dtype == T.float16:
+        n8_weight = new_qweight & np.int32(0xFF0000FF)
+        n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16
+        n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8
+        return n8_weight.view(np.int8)
+    elif nbits == 1 and target_dtype == T.float16:
+        n8_weight = new_qweight & 0xF000000F
+        n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8
+        n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16
+        n8_weight |= ((new_qweight & 0x0000F000) >> 12) << 24
+        n8_weight |= ((new_qweight & 0x000F0000) >> 16) << 4
+        n8_weight |= ((new_qweight & 0x00F00000) >> 20) << 12
+        n8_weight |= ((new_qweight & 0x0F000000) >> 24) << 20
+
+    return new_qweight.view(np.int8)
+
+
+def assert_bitnet_158_int8xint2_prefill_correctness(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding=True):
+    program = bitnet_158_int8xint2_prefill(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding)
+    print(program)
+    kernel = tilelang.compile(program)
+    src_code = kernel.get_kernel_source()
+    # src_code is the generated cuda source
+    assert src_code is not None
+    print(src_code)
+    A = torch.randint(0, 4, (M, K), device="cuda", dtype=getattr(torch, in_dtype))
+    B = torch.randint(0, 2, (N, K), device="cuda", dtype=getattr(torch, in_dtype))
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    qw = general_compress(B.cpu().numpy(), source_bits=2, storage_dtype=np.int8)
+    qw = interleave_weight(qw, 2, target_dtype=in_dtype)
+    qw = torch.from_numpy(qw).to(device="cuda")
+
+    kernel(A, qw, C)
+    # Get Reference Result
+    ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, accum_dtype))
+
+    print(ref_c)
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+    assert_bitnet_158_int8xint2_prefill_correctness(256, 256, 256, T.int8, T.int32, T.int32)

examples/bitnet-1.58b/kernel_benchmark/tl_int8xint8.py

+import torch
+import torch.backends
+from bitblas import tvm as tvm
+from tvm import DataType
+from tvm import tl as TL
+import tvm.tl.language as T
+from bitblas.tl.utils import get_swizzle_layout
+from bitblas.tl.mma_macro_generator import (
+    TensorCoreIntrinEmitter,
+)
+from bitblas.base import simplify_prim_func
+
+torch.manual_seed(0)
+
+
+def make_swizzle_layout(shared_buf):
+    dtype = shared_buf.dtype
+    shape = shared_buf.shape
+
+    can_swizzle = shape[-1] * DataType(dtype).bits == 512
+    if not can_swizzle:
+        return T.Layout(shape, lambda *args: args)
+
+    def transform_func(i, j):
+        new_warp_i, new_warp_j = get_swizzle_layout(i, j, shape[-1], dtype)
+        return [new_warp_i, new_warp_j]
+
+    return T.Layout(shape, transform_func)
+
+
+@simplify_prim_func
+def tl_matmul(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+):
+    assert in_dtype in [
+        T.float16,
+        T.int8,
+    ], "Currently only float16 and int8 are supported"
+    assert out_dtype in [
+        T.float16,
+        T.float32,
+        T.int32,
+    ], "Currently only float16, float32 and int32 are supported"
+
+    micro_size_x = micro_size_y = micro_size_k = 16
+
+    if out_dtype == T.int32:
+        micro_size_k = 32
+
+    # This is a debug config
+    block_row_warps = 2
+    block_col_warps = 2
+    warp_row_tiles = 64
+    warp_col_tiles = 64
+    chunk = 32 if in_dtype == T.float16 else 64
+    shared_scope = "shared.dyn"
+
+    # Pipeline Stage
+    stage = 2
+
+    block_M = block_row_warps * warp_row_tiles
+    block_N = block_col_warps * warp_col_tiles
+    block_K = chunk
+
+    A_shape = (M, K)
+    B_shape = (N, K)
+    A_shared_shape = (block_M, block_K)
+    B_shared_shape = (block_N, block_K)
+    C_shared_shape = (
+        block_M // micro_size_x,
+        block_N // micro_size_y,
+        micro_size_x,
+        micro_size_y,
+    )
+
+    warp_size = 32
+    threads = warp_size * (block_row_warps * block_col_warps)
+    local_size_a = (micro_size_x * micro_size_k) // warp_size
+    local_size_b = (micro_size_y * micro_size_k) // warp_size
+    local_size_c = (micro_size_x * micro_size_y) // warp_size
+    warp_rows = warp_row_tiles // micro_size_x
+    warp_cols = warp_col_tiles // micro_size_y
+
+    # MMA Wrapper to Auto Generate Code for MMA
+    mma_emitter = TensorCoreIntrinEmitter(
+        a_dtype=in_dtype,
+        b_dtype=in_dtype,
+        accum_dtype=accum_dtype,
+        a_transposed=False,
+        b_transposed=True,
+        block_row_warps=block_row_warps,
+        block_col_warps=block_col_warps,
+        warp_row_tiles=warp_row_tiles,
+        warp_col_tiles=warp_col_tiles,
+        chunk=chunk,
+    )
+
+    @T.prim_func
+    def main(
+        A: T.Buffer(A_shape, in_dtype),
+        B: T.Buffer(B_shape, in_dtype),
+        C: T.Buffer((M, N), out_dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
+            A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
+            C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
+            A_local = T.alloc_local((warp_rows * local_size_a), in_dtype)
+            B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
+            C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
+
+            thread_bindings = T.thread_binding(0, threads, "threadIdx.x")
+
+            T.annotate_layout(
+                {
+                    A_shared: make_swizzle_layout(A_shared),
+                    B_shared: make_swizzle_layout(B_shared),
+                }
+            )
+
+            # Improve L2 Cache
+            T.use_swizzle(panel_size=10)
+
+            T.clear(C_local)
+
+            for ko in T.Pipelined((K // block_K), num_stages=stage):
+                # Load A into shared memory
+                for i, k in T.Parallel(block_M, block_K):
+                    A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
+
+                # Load B into shared memory
+                for j, k in T.Parallel(block_N, block_K):
+                    B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
+
+                for ki in T.serial(0, (block_K // micro_size_k)):
+                    # Load A into fragment
+                    mma_emitter.ldmatrix_a(
+                        A_local,
+                        A_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                    )
+
+                    # Load B into fragment
+                    mma_emitter.ldmatrix_b(
+                        B_local,
+                        B_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                    )
+
+                    # Perform Matrix Multiplication
+                    mma_emitter.mma(A_local, B_local, C_local)
+
+            # Perform STMatrix
+            mma_emitter.stmatrix(
+                C_local,
+                C_shared,
+                thread_bindings=thread_bindings,
+            )
+
+            # Store shared into global
+            for i, j in T.Parallel(block_M, block_N):
+                C[by * block_M + i, bx * block_N + j] = C_shared[
+                    i // micro_size_x,
+                    j // micro_size_y,
+                    i % micro_size_x,
+                    j % micro_size_y,
+                ]
+
+    return main
+
+
+def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype):
+    matmul = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype)
+    mod, params = TL.lower(matmul)
+    src_code = mod.imported_modules[0].get_source()
+    # src_code is the generated cuda source
+    assert src_code is not None
+    print(src_code)
+    if in_dtype == T.int8:
+        A = torch.randint(-7, 7, (M, K), device="cuda", dtype=torch.int8)
+        B = torch.randint(-7, 7, (N, K), device="cuda", dtype=torch.int8)
+    else:
+        A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
+        B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
+
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    mod = TL.Profiler(mod, params, [], TL.TensorSupplyType.Integer)
+
+    mod(A, B, C)
+
+    latency = mod.do_bench(mod.func, warmup=25)
+    print(f"Latency: {latency}")
+    # Ensure that the latency is not None
+    assert latency is not None
+
+    # Get Reference Result
+    ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, accum_dtype))
+    print(C)
+    print(ref_c)
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
+def test_assert_tl_matmul():
+    assert_tl_matmul_correctness(128, 128, 128, T.float16, T.float16, T.float16)
+    assert_tl_matmul_correctness(128, 256, 256, T.float16, T.float32, T.float32)
+
+
+if __name__ == "__main__":
+    # bitblas.testing.main()
+    # assert_tl_matmul_correctness(128, 128, 128, T.float16, T.float16, T.float16)
+    # assert_tl_matmul_correctness(128, 128, 128, T.int8, T.int32, T.int32)
+    assert_tl_matmul_correctness(16384, 16384, 16384, T.int8, T.int32, T.int32)

examples/bitnet-1.58b/load_from_quantized.py

+import torch
+import bitblas
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+import os
+from transformers import GenerationConfig
+import time
+
+filepath = os.path.abspath(__file__)
+dirpath = os.path.dirname(filepath)
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+model_name_or_path = "BitBLASModel/open_llama_3b_1.58bits"
+saved_model_path = os.path.join(dirpath, "models", f"{model_name_or_path}_bitblas")
+
+
+def generate_text(model, tokenizer, prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.lm_head.weight.device)
+    # Generate cos and sin values
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    end_time = time.time()
+
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    generation_time = end_time - start_time
+    num_tokens = len(output_ids[0])
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_text
+
+
+def main():
+    # load quantized model
+    qmodel = (
+        BitnetForCausalLM.from_quantized(
+            saved_model_path,
+        )
+        .cuda()
+        .half()
+    )
+    tokenizer = BitnetTokenizer.from_pretrained(model_name_or_path, use_fast=False)
+    # print("original model generated text:")
+    # print(generate_text(model, tokenizer, "Hi, ", max_length=100))
+    input_ids = torch.ones((1, 1), dtype=torch.long).cuda()
+    # naive model inference
+    output = qmodel(input_ids)
+    print("original model output:", output)
+    print("quantized model generated text:")
+    print(generate_text(qmodel, tokenizer, "Hi, ", max_length=100))
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/maint/README.md

+---
+license: mit
+---
+
+
+This is a BitBLAS Implementation for the reproduced 1.58bit model from [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B). We replaced the original simulated Int8x3bit Quantized Inference Kernel with BitBLAS INT8xINT2 Kernel. We also evaluated the model's correctness and performance through `eval_correctness.py` and `benchmark_inference_latency.py`.
+
+## Latest News
+
+- 08/09/2024 ✨: We provide a more efficient implementation for bitnet with vLLM, which should use special model checkpoints, to make the ckpt and study how to deploy, please checkout [Make Checkpoints for vLLM](#make-checkpoints-for-vllm).
+
+## Make Checkpoints for vLLM
+
+We provide two scripts to make the checkpoints for vLLM. The first script is `generate_bitnet_model_native_format.sh`, which is used to make a checkpoint with fp16 uncompressed metaadta, the main difference with the original checkpoint is the `quant_config.json`, which allow vLLM to load the model and execute with a quant extension.
+
+```bash
+# move to the integration directory
+cd /root/to/BitBLAS/integration/BitNet
+# make the checkpoint
+./maint/generate_bitnet_model_native_format.sh
+# the output ckpy will be saved in the `./models/ckpt_bitnet_b1_58-3B` directory
+```
+
+The second script is `generate_bitnet_model_bitblas_format.sh`, which is used to make a checkpoint with BitBLAS compressed metadata, which can avoid the online dequantize sage for the profiling of vLLM, which lead to more efficient memory utilization.
+
+```bash
+./maint/generate_bitnet_model_bitblas_format.sh ./models/ckpt_bitnet_b1_58-3B ./models/ckpt_bitnet_b1_58-3B_bitblas
+# the output ckpy will be saved in the `./models/ckpt_bitnet_b1_58-3B_bitblas` directory
+```
+
+Finnaly, you can use the ckpt in vLLM with:
+
+```bash
+cd vllm_workspace
+# inference with the ckpt with fp16 uncompressed metadata
+python3 inference_with_native_format.py
+# inference with the ckpt with BitBLAS compressed metadata
+python3 inference_with_bitblas_format.py
+```
+
+## BitBLAS Results
+
+### Performance
+
+**Note:** To reproduce the results of BitBLAS, Please checkout the `benchmark_inference_latency.py`. To reproduce the results of the original model, Please checkout the [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B) repo.
+
+|      Model      | Device | batchsize | in_seq |   model  | bitnet-1.58b-3b-huggingface | bitnet-1.58b-3b-bitblas |
+|:---------------:|:------:|:---------:|:------:|:--------:|:---------------------------:|:-----------------------:|
+| bitnet_b1_58-3B |  A100  |     1     |    1   | LLAMA-3B |         177.6729107         |       64.17962909       |
+| bitnet_b1_58-3B |  A100  |    128    |    1   | LLAMA-3B |         188.6145592         |       63.48158518       |
+| bitnet_b1_58-3B |  A100  |     1     |  2048  | LLAMA-3B |         348.7066031         |       202.6877999       |
+
+### On-the-Fly GPU Memory Footprint
+
+We measured the GPU memory footprint through the `nvidia-smi` command. Please checkout `nvidia_measure_memory.sh` to get the real-time GPU memory usage. And then start a `benchmark_model_10k_loops.py` workload to measure the overall GPU memory usage.
+
+|    **Model**    | **Device** | **batchsize** | **in_seq** | **bitnet-1.58b-3b-huggingface** | **bitnet-1.58b-3b-bitblas** |
+|:---------------:|:----------:|:-------------:|:----------:|:-------------------------------:|:---------------------------:|
+| bitnet_b1_58-3B |    A100    |       1       |      1     |             7595 MB             |           1729 MB           |
+| bitnet_b1_58-3B |    A100    |      128      |      1     |             7677 MB             |           1789 MB           |
+| bitnet_b1_58-3B |    A100    |       1       |    2048    |             8731 MB             |           3163 MB           |
+
+## PPL and Zero-shot Accuracy
+
+The number is Reported from the [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B), Please checkout the `eval_ppl.py`.
+
+PPL and zero-shot accuracy:
+| Models | PPL| ARCe| ARCc| HS | BQ | OQ | PQ | WGe | Avg
+|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
+| FP16 700M (reported) | 12.33 | 54.7 | 23.0 | 37.0 | 60.0 | 20.2 | 68.9 | 54.8 | 45.5 |
+| BitNet b1.58 700M (reported) | 12.87 | 51.8 | 21.4 | 35.1 | 58.2 | 20.0 | 68.1 | 55.2 | 44.3 |
+| BitNet b1.58 700M (reproduced) | 12.78 | 51.4 | 21.8 | 35.0 | 59.6 | 20.6 | 67.5 | 55.4 | 44.5 |
+| FP16 1.3B (reported)    | 11.25  | 56.9 | 23.5 | 38.5 | 59.1 | 21.6 | 70.0 | 53.9 | 46.2
+| BitNet b1.58 1.3B (reported)    | 11.29  | 54.9 | 24.2 | 37.7 | 56.7 | 19.6 | 68.8 | 55.8 | 45.4 |
+| BitNet b1.58 1.3B (reproduced)    | 11.19 | 55.8 | 23.7 | 37.6 | 59.0 | 20.2 | 69.2 | 56.0 | 45.9
+| FP16 3B (reported)    | 10.04   | 62.1 | 25.6 | 43.3 | 61.8 | 24.6 | 72.1 | 58.2 | 49.7
+| BitNet b1.58 3B (reported)    | 9.91   | 61.4 | 28.3 | 42.9 | 61.5 | 26.6 | 71.5 | 59.3 | 50.2
+| BitNet b1.58 3B (reproduced)    | 9.88 | 60.9 | 28.0 | 42.3 | 58.3 | 26.0 | 71.4 | 60.3 | 49.6 |
+
+The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors.
+
+## Citations
+
+```bibtex
+@article{ma2024era,
+  title={The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits},
+  author={Ma, Shuming and Wang, Hongyu and Ma, Lingxiao and Wang, Lei and Wang, Wenhui and Huang, Shaohan and Dong, Li and Wang, Ruiping and Xue, Jilong and Wei, Furu},
+  journal={arXiv preprint arXiv:2402.17764},
+  year={2024}
+}
+```
\ No newline at end of file

examples/bitnet-1.58b/maint/create_bitblas_ckpt.py

+import argparse
+import torch
+import bitblas
+from transformers.utils.hub import cached_file
+import os
+from transformers import GenerationConfig
+import time
+import json
+
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)) + "/../")
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+
+filepath = os.path.abspath(__file__)
+dirpath = os.path.dirname(filepath)
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name_or_path", type=str, default="1bitLLM/bitnet_b1_58-3B")
+parser.add_argument("--saved_model_path", type=str, default=None)
+args = parser.parse_args()
+
+model_name_or_path = args.model_name_or_path
+saved_model_path = (
+    os.path.join(dirpath, "models", f"{model_name_or_path}_bitblas") if args.saved_model_path is None else args.saved_model_path
+)
+
+
+def generate_text(model, tokenizer, prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.lm_head.weight.device)
+    # Generate cos and sin values
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    end_time = time.time()
+
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    generation_time = end_time - start_time
+    num_tokens = len(output_ids[0])
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_text
+
+
+def main():
+    model = (
+        BitnetForCausalLM.from_pretrained(
+            model_name_or_path,
+            use_flash_attention_2=False,
+            torch_dtype=torch.float16,
+        )
+        .cuda()
+        .half()
+    )
+    tokenizer = BitnetTokenizer.from_pretrained(model_name_or_path, use_fast=False)
+
+    # print("original model generated text:")
+    # print(generate_text(model, tokenizer, "Hi, ", max_length=100))
+    input_ids = torch.ones((1, 1), dtype=torch.long).cuda()
+    # naive model inference
+    output = model(input_ids)
+    print("original model output:", output)
+
+    model.quantize(fuse_qkv=True, fuse_gateup=True)
+    print("original model generated text:")
+    print(generate_text(model, tokenizer, "Hi, ", max_length=100))
+
+    model.save_pretrained(saved_model_path)
+
+    # load quant config
+    quant_config_path = cached_file(model_name_or_path, "quantize_config.json")
+    with open(quant_config_path, "r") as f:
+        quant_config = json.load(f)
+    print("quant config:")
+    print(quant_config)
+    quant_config["checkpoint_format"] = "bitblas"
+    quant_config["fuse_qkv"] = True
+    quant_config["fuse_gateup"] = True
+
+    # save quant config
+    quant_config_path = os.path.join(saved_model_path, "quantize_config.json")
+    with open(quant_config_path, "w") as f:
+        json.dump(quant_config, f)
+    print("quant config saved to:", quant_config_path)
+
+    # copy benchmark filed into saved model path
+    file_list = [
+        "configuration_bitnet.py",
+        "eval_utils.py",
+        "modeling_bitnet.py",
+        "tokenization_bitnet.py",
+        "utils_quant.py",
+        "README.md",
+    ]
+    for file in file_list:
+        file_path = cached_file(model_name_or_path, file)
+        os.system(f"cp {file_path} {saved_model_path}")
+    # load quantized model
+    qmodel = (
+        BitnetForCausalLM.from_quantized(
+            saved_model_path,
+        )
+        .cuda()
+        .half()
+    )
+    print("quantized model generated text:")
+    print(generate_text(qmodel, tokenizer, "Hi, ", max_length=100))
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/maint/generate_bitnet_model_bitblas_format.sh

+# retrieve the native model input and saved model directory
+MODEL_DIR=$1
+SAVED_MODEL_DIR=$2
+
+# check if the model directory exists
+if [ ! -d "$MODEL_DIR" ]; then
+    echo "Model directory does not exist!"
+    exit 1
+fi
+
+# if the saved model directory does not exist, create it
+# if SAVED_MODEL_DIR is not provided, we do not pass it to the script
+if [ -z "$SAVED_MODEL_DIR" ]; then
+  python ./maint/create_bitblas_ckpt.py --model_name_or_path $MODEL_DIR
+else
+  if [ ! -d "$SAVED_MODEL_DIR" ]; then
+    mkdir -p $SAVED_MODEL_DIR
+  fi
+  python ./maint/create_bitblas_ckpt.py --model_name_or_path $MODEL_DIR --saved_model_path $SAVED_MODEL_DIR
+fi
+
+# get the realpath of the saved model directory
+SAVED_MODEL_DIR=$(realpath $SAVED_MODEL_DIR)
+
+# cp files
+cp $MODEL_DIR/quantize_config.json $SAVED_MODEL_DIR/
+cp $MODEL_DIR/tokenizer.json $SAVED_MODEL_DIR/
+cp $MODEL_DIR/tokenizer.model $SAVED_MODEL_DIR/
+cp $MODEL_DIR/tokenizer_config.json $SAVED_MODEL_DIR/
+
+echo "Model has been converted and save to $SAVED_MODEL_DIR"

examples/bitnet-1.58b/maint/generate_bitnet_model_native_format.sh

+# require git lfs
+if ! command -v git-lfs &> /dev/null; then
+    echo "Please install git-lfs first by running 'sudo apt install git-lfs'"
+    exit 1
+fi
+
+mkdir -p models
+
+cd models
+
+# download the model
+git clone https://huggingface.co/1bitLLM/bitnet_b1_58-3B ckpt_bitnet_b1_58-3B --depth 1
+
+# copy quantized config into the model directory
+cp ../maint/quantize_config.json ckpt_bitnet_b1_58-3B
+
+# copy README.md into the model directory
+cp ../maint/README.md ckpt_bitnet_b1_58-3B
+
+# get the realpath of the model directory
+MODEL_DIR=$(realpath ckpt_bitnet_b1_58-3B)
+
+cd ..
+
+echo "Model has been converted and save to $MODEL_DIR"

examples/bitnet-1.58b/maint/quantize_config.json

+{
+    "bits": 2,
+    "desc_act": false,
+    "static_groups": false,
+    "sym": true,
+    "lm_head": false,
+    "model_name_or_path": "1bitLLM/bitnet_b1_58-3B",
+    "quant_method": "bitnet",
+    "checkpoint_format": "bitnet"
+}
\ No newline at end of file

examples/bitnet-1.58b/maint/upload_models.sh

+MODEL_DIR=$1
+REMOTE_DIR=$2
+
+if [ ! -d "$MODEL_DIR" ]; then
+    echo "Model directory does not exist!"
+    exit 1
+fi
+
+cd $MODEL_DIR
+if [ ! -d ".git" ]; then
+    rm -rf .git
+fi
+
+git init
+
+git checkout -b main
+
+git lfs install
+
+git lfs track *.bin
+
+git lfs track *.safetensors
+
+git add .
+
+git commit -m "Initial commit"
+
+git remote add origin $REMOTE_DIR
+
+huggingface-cli lfs-enable-largefiles .
+
+git fetch origin
+
+git push -f --set-upstream origin main

examples/bitnet-1.58b/modeling_bitnet.py

+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LLaMA model."""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from configuration_bitnet import BitnetConfig
+from utils_quant import BitLinear, BitLinearBitBLAS
+from transformers.utils.hub import cached_file
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa: F401
+
+
+def find_layers(module, layers=None, name=""):
+    if not layers:
+        layers = [nn.Linear]
+    for layer in layers:
+        if isinstance(module, layer):
+            return {name: module}
+    res = {}
+    for name1, child in module.named_children():
+        res.update(find_layers(child, layers=layers, name=name + "." + name1 if name != "" else name1))
+    return res
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "BitnetConfig"
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class BitnetRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        BitnetRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(BitnetRMSNorm)
+
+
+class BitnetRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+        t = t / self.scaling_factor
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
+        self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
+
+    @property
+    def sin_cached(self):
+        logger.warning_once(
+            "The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
+            "the forward method of RoPE from now on instead. It is not used in the `BitnetAttention` class"
+        )
+        return self._sin_cached
+
+    @property
+    def cos_cached(self):
+        logger.warning_once(
+            "The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
+            "the forward method of RoPE from now on instead. It is not used in the `BitnetAttention` class"
+        )
+        return self._cos_cached
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class BitnetMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = BitLinear(
+            self.hidden_size,
+            self.intermediate_size,
+            bias=False,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.up_proj = BitLinear(
+            self.hidden_size,
+            self.intermediate_size,
+            bias=False,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.down_proj = BitLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.ffn_layernorm = BitnetRMSNorm(self.intermediate_size, eps=config.rms_norm_eps)
+
+    def forward(self, x):
+        x = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
+        x = self.ffn_layernorm(x)
+        x = self.down_proj(x)
+        return x
+
+
+class BitnetMLPFuseGateUp(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_up_proj = BitLinear(
+            self.hidden_size,
+            self.intermediate_size * 2,
+            bias=False,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.down_proj = BitLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.ffn_layernorm = BitnetRMSNorm(self.intermediate_size, eps=config.rms_norm_eps)
+
+    @classmethod
+    def from_bit_mlp(cls, bit_mlp: BitnetMLP):
+        module = cls(bit_mlp.config)
+        # assign the weights
+        module.gate_up_proj.weight = nn.Parameter(torch.cat([bit_mlp.gate_proj.weight, bit_mlp.up_proj.weight], dim=0))
+        module.down_proj = bit_mlp.down_proj
+        module.ffn_layernorm = bit_mlp.ffn_layernorm
+        return module
+
+    def forward(self, x):
+        gate_up = self.gate_up_proj(x)
+        gate, up = torch.chunk(gate_up, chunks=2, dim=-1)
+        x = self.act_fn(gate) * up
+        x = self.ffn_layernorm(x)
+        x = self.down_proj(x)
+        return x
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class BitnetAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = BitLinear(
+            self.hidden_size,
+            self.num_heads * self.head_dim,
+            bias=config.attention_bias,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.k_proj = BitLinear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.v_proj = BitLinear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.o_proj = BitLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=config.attention_bias,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self._init_rope()
+        self.inner_attn_ln = BitnetRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = BitnetRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            raise NotImplementedError
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is {attn_output.size()}")
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.inner_attn_ln(attn_output)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class BitnetAttentionQKVFused(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`: {self.num_heads})."
+            )
+
+        self.qkv_proj = BitLinear(
+            self.hidden_size,
+            self.num_heads * self.head_dim + (self.num_key_value_heads * self.head_dim) * 2,
+            bias=config.attention_bias,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self.o_proj = BitLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=config.attention_bias,
+            weight_bits=config.weight_bits,
+            input_bits=config.input_bits,
+        )
+        self._init_rope()
+        self.inner_attn_ln = BitnetRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = BitnetRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            raise NotImplementedError
+
+    @classmethod
+    def from_bit_attention(cls, bit_attention: BitnetAttention):
+        module = cls(bit_attention.config, bit_attention.layer_idx)
+        # assign the weights
+        module.qkv_proj.weight = nn.Parameter(
+            torch.cat([bit_attention.q_proj.weight, bit_attention.k_proj.weight, bit_attention.v_proj.weight], dim=0)
+        )
+        if bit_attention.q_proj.bias is not None and bit_attention.k_proj.bias is not None and bit_attention.v_proj.bias is not None:
+            module.qkv_proj.bias = nn.Parameter(
+                torch.cat([bit_attention.q_proj.bias, bit_attention.k_proj.bias, bit_attention.v_proj.bias], dim=0)
+            )
+        module.o_proj = bit_attention.o_proj
+        module.inner_attn_ln = bit_attention.inner_attn_ln
+        if bit_attention.config.rope_scaling is None:
+            module.rotary_emb = bit_attention.rotary_emb
+        return module
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+        qkv_states = self.qkv_proj(hidden_states)
+        query_states, key_states, value_states = torch.split(
+            qkv_states,
+            [self.num_heads * self.head_dim, self.num_key_value_heads * self.head_dim, self.num_key_value_heads * self.head_dim],
+            dim=-1,
+        )
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is {attn_output.size()}")
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.inner_attn_ln(attn_output)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class BitnetFlashAttention2(BitnetAttention):
+    """
+    Bitnet flash attention module. This module inherits from `BitnetAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (BitnetRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate)
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.inner_attn_ln(attn_output)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`float`):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in BitnetFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal)
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k)
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+LLAMA_ATTENTION_CLASSES = {
+    "eager": BitnetAttention,
+    "flash_attention_2": BitnetFlashAttention2,
+}
+
+
+class BitnetDecoderLayer(nn.Module):
+    def __init__(self, config: BitnetConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = BitnetMLP(config)
+        self.input_layernorm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    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]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        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.`",
+                stacklevel=2,
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(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.post_attention_layernorm(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
+
+
+LLAMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`BitnetConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class BitnetPreTrainedModel(PreTrainedModel):
+    config_class = BitnetConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["BitnetDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = False
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
+        if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
+            raise ValueError(
+                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+            )
+
+        for layer in self.model.layers:
+            device = layer.input_layernorm.weight.device
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                dtype = self.config._pre_quantization_dtype
+            else:
+                dtype = layer.self_attn.o_proj.weight.dtype
+            layer.self_attn.past_key_value = cache_cls(self.config, max_batch_size, max_cache_len, device=device, dtype=dtype)
+
+    def _reset_cache(self):
+        for layer in self.model.layers:
+            layer.self_attn.past_key_value = None
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`BitnetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`BitnetTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class BitnetModel(BitnetPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BitnetDecoderLayer`]
+
+    Args:
+        config: BitnetConfig
+    """
+
+    def __init__(self, config: BitnetConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([BitnetDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+        self.norm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.")
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        past_seen_tokens = 0
+        if use_cache and not isinstance(past_key_values, StaticCache):
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_seen_tokens = past_key_values.get_seq_length()
+
+        if cache_position is None:
+            if isinstance(past_key_values, StaticCache):
+                raise ValueError("cache_position is a required argument when using StaticCache.")
+            cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device)
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
+    # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
+    # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
+    # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
+    def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if hasattr(self.layers[0].self_attn, "past_key_value"):  # static cache
+            target_length = self.config.max_position_embeddings
+        else:  # dynamic cache
+            target_length = attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else cache_position[-1] + 1
+
+        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+            if attention_mask.dim() == 2:
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
+                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+            elif attention_mask.dim() == 4:
+                # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
+                # cache. In that case, the 4D attention mask attends to the newest tokens only.
+                if attention_mask.shape[-2] < cache_position[0] + sequence_length:
+                    offset = cache_position[0]
+                else:
+                    offset = 0
+                mask_shape = attention_mask.shape
+                mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
+                causal_mask[: mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]] = mask_slice
+
+        return causal_mask
+
+
+class BitnetForCausalLM(BitnetPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = BitnetModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.quantized = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import LlamaTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Bitnet-2-7b-hf")
+        >>> tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Bitnet-2-7b-hf")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs
+    ):
+        # With static cache, the `past_key_values` is None
+        # TODO joao: standardize interface for the different Cache classes and remove of this if
+        has_static_cache = False
+        if past_key_values is None:
+            past_key_values = getattr(self.model.layers[0].self_attn, "past_key_value", None)
+            has_static_cache = past_key_values is not None
+
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
+                max_cache_length = (
+                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
+                    if past_key_values.get_max_length() is not None
+                    else None
+                )
+                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
+            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length:
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids")
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
+        else:
+            cache_position = cache_position[-input_length:]
+
+        if has_static_cache:
+            past_key_values = None
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
+        return reordered_past
+
+    @staticmethod
+    def recursive_set(model, name, attr):
+        """
+        set layers.25.mlp.up_proj to attr
+        """
+
+        names = name.split(".")
+        obj = model
+        for n in names[:-1]:
+            obj = getattr(obj, n)
+        setattr(obj, names[-1], attr)
+
+    def quantize(self, fuse_qkv=True, fuse_gateup=True):
+        for name, module in self.model.named_modules():
+            # if is bitnet layer
+            if fuse_qkv and isinstance(module, BitnetAttention):
+                # create quantized version of the layer
+                print("Replacing BitnetAttention", name)
+                bitnet_attenion_qkv_fused = BitnetAttentionQKVFused.from_bit_attention(module)
+                self.recursive_set(self.model, name, bitnet_attenion_qkv_fused)
+            if fuse_gateup and isinstance(module, BitnetMLP):
+                # create quantized version of the layer
+                print("Replacing BitnetMLP", name)
+                bitnet_mlp_fused = BitnetMLPFuseGateUp.from_bit_mlp(module)
+                self.recursive_set(self.model, name, bitnet_mlp_fused)
+        for name, module in self.model.named_modules():
+            # if is bitnet layer
+            if isinstance(module, BitLinear):
+                # create quantized version of the layer
+                print("Quantizing module", name)
+                if name.endswith(".qkv_proj"):
+                    bitblas_linear = BitLinearBitBLAS.from_bit_linear(module, weight_group=3)
+                elif name.endswith(".gate_up_proj"):
+                    bitblas_linear = BitLinearBitBLAS.from_bit_linear(module, weight_group=2)
+                else:
+                    bitblas_linear = BitLinearBitBLAS.from_bit_linear(module)
+                print("Replacing module", name, "with a quantized version")
+                self.recursive_set(self.model, name, bitblas_linear)
+        self.quantized = True
+
+    def _post_process_weights(self):
+        for name, module in self.model.named_modules():
+            if hasattr(module, "post_process_weights"):
+                print("Post processing weights for module", name)
+                module.post_process_weights()
+
+    def _replace_weight_param_with_qweight(self):
+        for name, module in self.model.named_modules():
+            if hasattr(module, "replace_weight_param_with_qweight"):
+                print("Replacing weight param with qweight for module", name)
+                module.replace_weight_param_with_qweight()
+
+    @classmethod
+    def from_quantized(
+        cls,
+        model_name_or_path: Optional[str],
+        trust_remote_code: bool = False,
+        **kwargs,
+    ):
+        """load quantized model from local disk"""
+        # Parameters related to loading from Hugging Face Hub
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", False)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", "")
+        commit_hash = kwargs.pop("_commit_hash", None)
+
+        cached_file_kwargs = {
+            "cache_dir": cache_dir,
+            "force_download": force_download,
+            "proxies": proxies,
+            "resume_download": resume_download,
+            "local_files_only": local_files_only,
+            "use_auth_token": use_auth_token,
+            "revision": revision,
+            "subfolder": subfolder,
+            "_raise_exceptions_for_missing_entries": False,
+            "_commit_hash": commit_hash,
+        }
+        # == step1: prepare configs and file names == #
+        config: BitnetConfig = BitnetConfig.from_pretrained(
+            model_name_or_path,
+            trust_remote_code=trust_remote_code,
+            **cached_file_kwargs,
+        )
+        # load quantize config
+        quantize_file = cached_file(model_name_or_path, "quantize_config.json")
+        assert quantize_file is not None, "quantize config file not found"
+        import json
+
+        # get quantize format
+        with open(quantize_file, "r") as f:
+            quant_config = json.load(f)
+        checkpoint_format = quant_config["checkpoint_format"]
+        assert checkpoint_format in ["bitblas"], "quantize format not supported"
+        fuse_qkv = quant_config.get("fuse_qkv", True)
+        fuse_gateup = quant_config.get("fuse_gateup", True)
+
+        import accelerate
+
+        if checkpoint_format == "bitblas":
+            model = cls(config)
+            for name, module in model.named_modules():
+                # if is bitnet layer
+                if fuse_qkv and isinstance(module, BitnetAttention):
+                    # create quantized version of the layer
+                    print("Replacing BitnetAttention", name)
+                    bitnet_attenion_qkv_fused = BitnetAttentionQKVFused.from_bit_attention(module)
+                    model.recursive_set(model, name, bitnet_attenion_qkv_fused)
+                if fuse_gateup and isinstance(module, BitnetMLP):
+                    # create quantized version of the layer
+                    print("Replacing BitnetMLP", name)
+                    bitnet_mlp_fused = BitnetMLPFuseGateUp.from_bit_mlp(module)
+                    model.recursive_set(model, name, bitnet_mlp_fused)
+            for name, module in model.named_modules():
+                if isinstance(module, BitLinear):
+                    # create quantized version of the layer
+                    print("Quantizing module", name)
+                    bitblas_linear = BitLinearBitBLAS.from_bit_linear(module)
+                    print("Replacing module", name, "with a quantized version")
+                    model.recursive_set(model, name, bitblas_linear)
+            accelerate.utils.modeling.load_checkpoint_in_model(
+                model,
+                checkpoint=model_name_or_path,
+                offload_state_dict=True,
+                offload_buffers=True,
+            )
+            return model
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`BitnetForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class BitnetForSequenceClassification(BitnetPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = BitnetModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+The Bitnet Model transformer with a span classification head on top for extractive question-answering tasks like
+SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class BitnetForQuestionAnswering(BitnetPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Bitnet
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = BitnetModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.transformer.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.transformer.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labeled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labeled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

examples/bitnet-1.58b/nvidia_measure_memory.sh

+nvidia-smi --query-gpu=memory.used --format=csv -lms 500