add tilelang

tilelang/original/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()

tilelang/original/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"

tilelang/original/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"

tilelang/original/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

tilelang/original/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

tilelang/original/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,
+        )

tilelang/original/examples/bitnet-1.58b/nvidia_measure_memory.sh

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

tilelang/original/examples/bitnet-1.58b/requirements.txt

+lm_eval==0.3.0
+flash_attn
+transformers==4.53.0

tilelang/original/examples/bitnet-1.58b/tokenization_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.
+"""Tokenization classes for LLaMA."""
+
+import os
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.convert_slow_tokenizer import import_protobuf
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+if TYPE_CHECKING:
+    from transformers.tokenization_utils_base import TextInput
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
+    },
+    "tokenizer_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
+    },
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "hf-internal-testing/llama-tokenizer": 2048,
+}
+SPIECE_UNDERLINE = "▁"
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+# fmt: off
+DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
+answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
+ that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
+correct. If you don't know the answer to a question, please don't share false information."""
+# fmt: on
+
+
+class BitnetTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Bitnet tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
+    no padding token in the original model.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+            attention mechanisms or loss computation.
+        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+
+        add_bos_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of sequences.
+        add_eos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of sequences.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not the default system prompt for Bitnet should be used.
+        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
+            Whether or not to add spaces between special tokens.
+        legacy (`bool`, *optional*):
+            Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
+            and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
+            example:
+
+            - `legacy=True`:
+            ```python
+            >>> from transformers import T5Tokenizer
+
+            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
+            >>> tokenizer.encode("Hello <extra_id_0>.")
+            [8774, 32099, 3, 5, 1]
+            ```
+            - `legacy=False`:
+            ```python
+            >>> from transformers import T5Tokenizer
+
+            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
+            >>> tokenizer.encode("Hello <extra_id_0>.")  # the extra space `[3]` is no longer here
+            [8774, 32099, 5, 1]
+            ```
+            Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
+        add_prefix_space (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+            other word.
+
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        use_default_system_prompt=False,
+        spaces_between_special_tokens=False,
+        legacy=None,
+        add_prefix_space=True,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
+
+        if legacy is None:
+            logger.warning_once(
+                f"You are using the default legacy behavior of the {self.__class__}. This is"
+                " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
+                " If you want to use the new behavior, set `legacy=False`. This should only be set if you understand what it"
+                " means, and thoroughly read the reason why this was added as explained in"
+                " https://github.com/huggingface/transformers/pull/24565"
+            )
+            legacy = True
+
+        self.legacy = legacy
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.use_default_system_prompt = use_default_system_prompt
+        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
+        self.add_prefix_space = add_prefix_space
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            use_default_system_prompt=use_default_system_prompt,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            legacy=legacy,
+            add_prefix_space=add_prefix_space,
+            **kwargs,
+        )
+
+    @property
+    def unk_token_length(self):
+        return len(self.sp_model.encode(str(self.unk_token)))
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
+    def get_spm_processor(self, from_slow=False):
+        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        if self.legacy or from_slow:  # no dependency on protobuf
+            tokenizer.Load(self.vocab_file)
+            return tokenizer
+
+        with open(self.vocab_file, "rb") as f:
+            sp_model = f.read()
+            model_pb2 = import_protobuf(f"The new behavior of {self.__class__.__name__} (with `self.legacy = False`)")
+            model = model_pb2.ModelProto.FromString(sp_model)
+            normalizer_spec = model_pb2.NormalizerSpec()
+            normalizer_spec.add_dummy_prefix = False
+            model.normalizer_spec.MergeFrom(normalizer_spec)
+            sp_model = model.SerializeToString()
+            tokenizer.LoadFromSerializedProto(sp_model)
+        return tokenizer
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
+    def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
+        """
+        Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
+        first token is special.
+        """
+        if self.legacy or len(text) == 0:
+            return super().tokenize(text, **kwargs)
+
+        text = text.replace(SPIECE_UNDERLINE, " ")
+        if self.add_prefix_space:
+            text = SPIECE_UNDERLINE + text
+
+        tokens = super().tokenize(text, **kwargs)
+
+        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
+            tokens = tokens[1:]
+        return tokens
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
+    def _tokenize(self, text, **kwargs):
+        """
+        Returns a tokenized string.
+
+        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
+        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
+        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
+        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
+        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
+        """
+        tokens = self.sp_model.encode(text, out_type=str)
+        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
+            return tokens
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        # since we manually add the prefix space, we have to remove it when decoding
+        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
+            tokens[0] = tokens[0][1:]
+
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for i, token in enumerate(tokens):
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special and i != 0 and self.legacy:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id
+
+    def create_token_type_ids_from_sequences(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+        if token_ids_1 is not None:
+            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+        return output
+
+    @property
+    def default_chat_template(self):
+        """
+        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
+        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
+        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
+        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
+        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
+        to fine-tune a model with more flexible role ordering!
+
+        The output should look something like:
+
+        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
+        <bos>[INST] Prompt [/INST]
+
+        The reference for this chat template is [this code
+        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
+        in the original repository.
+        """
+        logger.warning_once(
+            "\nNo chat template is defined for this tokenizer - using the default template "
+            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
+            "your model, please set `tokenizer.chat_template` to an appropriate template. "
+            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
+        )
+        template = (
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
+            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = false %}"
+            "{% endif %}"
+            "{% for message in loop_messages %}"  # Loop over all non-system messages
+            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
+            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
+            "{% endif %}"
+            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
+            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
+            "{% else %}"
+            "{% set content = message['content'] %}"
+            "{% endif %}"
+            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
+            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
+            "{% elif message['role'] == 'system' %}"
+            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ ' '  + content.strip() + ' ' + eos_token }}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
+        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+
+        return template

tilelang/original/examples/bitnet-1.58b/utils_quant.py

+# pylint: disable=missing-docstring, invalid-name
+"""This is modified from https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/main/utils_quant.py to work with BitBLAS."""
+
+import torch
+from torch import nn
+from bitblas.cache import global_operator_cache, get_database_path
+from bitblas import Matmul, MatmulConfig
+from bitblas import auto_detect_nvidia_target
+from logging import getLogger
+
+logger = getLogger(__name__)
+BITBLAS_TARGET = auto_detect_nvidia_target()
+BITBLAS_DATABASE_PATH = get_database_path()
+
+
+def weight_quant(weight, num_bits=1):
+    dtype = weight.dtype
+    weight = weight.float()
+    s = 1 / weight.abs().mean().clamp(min=1e-5)
+    result = (weight * s).round().clamp(-1, 1) / s
+    return result.type(dtype)
+
+
+def activation_quant(x, num_bits=8):
+    dtype = x.dtype
+    x = x.float()
+    Qn = -(2 ** (num_bits - 1))
+    Qp = 2 ** (num_bits - 1) - 1
+    s = Qp / x.abs().max(dim=-1, keepdim=True).values.clamp(min=1e-5)
+    result = (x * s).round().clamp(Qn, Qp) / s
+    return result.type(dtype)
+
+
+class BitLinearBitBLAS(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        weight_bits=1,
+        input_bits=8,
+        **kwargs,
+    ):
+        super().__init__()
+        """
+        RMSNorm is placed outside BitLinear
+        """
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight_bits = weight_bits
+        self.input_bits = input_bits
+        matmul_config = MatmulConfig(
+            N=self.out_features,  # N dimension
+            K=self.in_features,  # K dimension
+            A_dtype="int8",  # activation A dtype
+            W_dtype="int2",  # weight W dtype
+            accum_dtype="int32",  # accumulation dtype
+            out_dtype="float32",  # output dtype
+            layout="nt",  # matrix layout, "nt" indicates the layout of A is non-transpose and the layout of W is transpose
+            with_bias=False,  # bias
+            # configs for weight only quantization
+            group_size=None,  # setting for grouped quantization
+            with_scaling=False,  # setting for scaling factor
+            with_zeros=False,  # setting for zeros
+            zeros_mode=None,  # setting for how to calculating zeros
+        )
+        ENABLE_TUNING = True
+        self.bitblas_matmul = self._get_or_create_bitblas_operator(matmul_config, ENABLE_TUNING)
+
+        self.format = "bitnet"
+        self.Qp = 2 ** (self.input_bits - 1) - 1
+
+    def _get_or_create_bitblas_operator(self, config, enable_tuning):
+        if global_operator_cache.size() == 0:
+            global_operator_cache.load_from_database(BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
+            logger.info(f"Loaded {global_operator_cache.size()} operators from database.")
+
+        bitblas_matmul = global_operator_cache.get(config)
+        if bitblas_matmul is None:
+            # should disable tuning for the first time because we may require loading bitblas operator from database.
+            bitblas_matmul = Matmul(config, target=BITBLAS_TARGET, enable_tuning=False)
+            if enable_tuning:
+                bitblas_matmul.hardware_aware_finetune(topk=20)
+                global_operator_cache.add(config, bitblas_matmul)
+                global_operator_cache.save_into_database(BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
+                print("BitBLAS Tuning done, appended operator to global_operator_cache.")
+            else:
+                print("BitBLAS Operator created.")
+        else:
+            print("BitBLAS Operator found in global_operator_cache.")
+        return bitblas_matmul
+
+    def replace_weight_param_with_qweight(self):
+        if hasattr(self, "weight"):
+            del self.weight
+        quant_weight = torch.empty(self.bitblas_matmul.retrieve_weight_shape())
+        self.qweight = nn.Parameter(quant_weight, requires_grad=False)
+        self.format = "bitblas"
+
+    @classmethod
+    def from_bit_linear(cls, bitlinear, weight_group=1):
+        bitblas_linear = cls(bitlinear.in_features, bitlinear.out_features, weight_bits=1, input_bits=8)
+        sw, qweight = bitblas_linear.create_bitblas_weights(bitlinear.weight, weight_group)
+        bitblas_linear.register_buffer("qweight", qweight)
+        bitblas_linear.register_buffer("sw", sw)
+        if bitlinear.bias is not None:
+            bitblas_linear.register_buffer("bias", bitlinear.bias)
+        else:
+            bitblas_linear.bias = None
+        return bitblas_linear
+
+    def create_bitblas_weights(self, weight, weight_group=1):
+        if weight_group:
+            hidden_size = weight.size(0)
+            group_size = hidden_size // weight_group
+
+            sw_list = []
+            qweight_list = []
+
+            for i in range(weight_group):
+                start_idx = i * group_size
+                end_idx = (i + 1) * group_size
+
+                sw = 1 / weight[start_idx:end_idx].abs().mean().clamp(min=1e-5)
+                sw_list.append(sw.repeat(group_size))
+
+                qweight = self.weight_quant(weight[start_idx:end_idx]).detach()
+                qweight_list.append(qweight)
+
+            sw = torch.cat(sw_list, dim=0)
+            qweight = torch.cat(qweight_list, dim=0)
+        else:
+            sw = 1 / weight.abs().mean().clamp(min=1e-5)
+            qweight = self.weight_quant(weight).detach()
+        qweight = self.bitblas_matmul.transform_weight(qweight)
+        qweight = nn.Parameter(qweight, requires_grad=False)
+        return sw, qweight
+
+    def post_process_weights(self):
+        sw = 1 / self.weight.abs().mean().clamp(min=1e-5)
+        self.sw = sw
+        quant_weight = self.weight_quant(self.weight).detach()
+        quant_weight = self.bitblas_matmul.transform_weight(quant_weight)
+        # remove self.weight and replace it with quant_weight
+        if hasattr(self, "weight"):
+            del self.weight
+        self.qweight = nn.Parameter(quant_weight, requires_grad=False)
+        self.format = "bitblas"
+
+    @staticmethod
+    def weight_quant(weight):
+        weight = weight.float()
+        s = 1 / weight.abs().mean().clamp(min=1e-5)
+        result = (weight * s).round().clamp(-1, 1)
+        return result.type(torch.int8)
+
+    @torch.compile
+    def activation_quant(self, x, num_bits=8):
+        x = x.float()
+        Qn = -(2 ** (num_bits - 1))
+        Qp = 2 ** (num_bits - 1) - 1
+        s = Qp / x.abs().max(dim=-1, keepdim=True).values.clamp(min=1e-5)
+        result = (x * s).round().clamp(Qn, Qp)
+        return result.type(torch.int8), s
+
+    @torch.compile
+    def post_quant_process(self, input, si, sw):
+        out = input / si
+        out = out / sw
+        out = out.half()
+        return out
+
+    # for the correctness evaluation.
+    def native_forward(self, input):
+        quant_input = input + (activation_quant(input, self.input_bits) - input).detach()
+        quant_weight = self.weight + (weight_quant(self.weight, self.weight_bits) - self.weight).detach()
+
+        out = nn.functional.linear(quant_input, quant_weight)
+        if self.bias is not None:
+            out += self.bias.view(1, -1).expand_as(out)
+        return out
+
+    def forward_fp32_simulated(self, input):
+        quant_input, si = self.activation_quant(input, self.input_bits).detach()
+        quant_weight = self.weight_quant(self.weight).detach()
+
+        fp32_simulated_input = quant_input.float()
+        fp32_simulated_weight = quant_weight.float()
+        fp32_simulated_out = nn.functional.linear(fp32_simulated_input, fp32_simulated_weight)
+
+        sw = 1 / self.weight.abs().mean().clamp(min=1e-5)
+        # if / (si * sw) it will inf in some cases
+        out = fp32_simulated_out / si
+        out = out / sw
+        out = out.half()
+        if self.bias is not None:
+            out += self.bias.view(1, -1).expand_as(out)
+        return out
+
+    def forward(self, input):
+        # return self.forward_fp32_simulated(input)
+        quant_input, si = self.activation_quant(input, self.input_bits)
+        fp32_out = self.bitblas_matmul(quant_input, self.qweight)
+        sw = self.sw
+        # if / (si * sw) it will inf in some cases
+        out = self.post_quant_process(fp32_out, si, sw)
+
+        if self.bias is not None:
+            out += self.bias.view(1, -1).expand_as(out)
+        return out
+
+
+# Naive BitLinear from HuggingFace
+class BitLinear(nn.Linear):
+    def __init__(self, *kargs, weight_bits=1, input_bits=8, **kwargs):
+        super(BitLinear, self).__init__(*kargs, **kwargs)
+        """
+        RMSNorm is placed outside BitLinear
+        """
+        self.weight_bits = weight_bits
+        self.input_bits = input_bits
+
+    def forward(self, input):
+        quant_input = input + (activation_quant(input, self.input_bits) - input).detach()
+        quant_weight = self.weight + (weight_quant(self.weight, self.weight_bits) - self.weight).detach()
+
+        out = nn.functional.linear(quant_input, quant_weight)
+        if self.bias is not None:
+            out += self.bias.view(1, -1).expand_as(out)
+
+        return out

tilelang/original/examples/bitnet-1.58b/vllm_workspace/conftest.py

+import contextlib
+import gc
+import os
+import sys
+from collections import UserList
+from typing import Any, Dict, List, Optional, Tuple, TypedDict, TypeVar
+
+import pytest
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from transformers import (
+    AutoModelForCausalLM,
+    AutoModelForVision2Seq,
+    AutoTokenizer,
+    BatchEncoding,
+)
+
+from vllm import LLM, SamplingParams
+from vllm.assets.image import ImageAsset
+from vllm.config import TokenizerPoolConfig
+from vllm.distributed import destroy_distributed_environment, destroy_model_parallel
+from vllm.inputs import TextPrompt
+from vllm.logger import init_logger
+from vllm.sequence import SampleLogprobs
+from vllm.utils import cuda_device_count_stateless, is_cpu
+
+logger = init_logger(__name__)
+
+_TEST_DIR = os.path.dirname(__file__)
+_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
+_LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
+
+
+def _read_prompts(filename: str) -> List[str]:
+    with open(filename, "r") as f:
+        prompts = f.readlines()
+        return prompts
+
+
+class _ImageAssetPrompts(TypedDict):
+    stop_sign: str
+    cherry_blossom: str
+
+
+if sys.version_info < (3, 9):
+    # UserList cannot be subscripted
+    class _ImageAssetsBase(UserList):
+        pass
+
+else:
+
+    class _ImageAssetsBase(UserList[ImageAsset]):
+        pass
+
+
+class _ImageAssets(_ImageAssetsBase):
+    def __init__(self) -> None:
+        super().__init__(
+            [
+                ImageAsset("stop_sign"),
+                ImageAsset("cherry_blossom"),
+            ]
+        )
+
+    def prompts(self, prompts: _ImageAssetPrompts) -> List[str]:
+        """
+        Convenience method to define the prompt for each test image.
+
+        The order of the returned prompts matches the order of the
+        assets when iterating through this object.
+        """
+        return [prompts["stop_sign"], prompts["cherry_blossom"]]
+
+
+IMAGE_ASSETS = _ImageAssets()
+"""Singleton instance of :class:`_ImageAssets`."""
+
+
+def cleanup():
+    destroy_model_parallel()
+    destroy_distributed_environment()
+    with contextlib.suppress(AssertionError):
+        torch.distributed.destroy_process_group()
+    gc.collect()
+    if not is_cpu():
+        torch.cuda.empty_cache()
+
+
+@pytest.fixture()
+def should_do_global_cleanup_after_test(request) -> bool:
+    """Allow subdirectories to skip global cleanup by overriding this fixture.
+    This can provide a ~10x speedup for non-GPU unit tests since they don't need
+    to initialize torch.
+    """
+
+    if not request.node.get_closest_marker("skip_global_cleanup"):
+        return False
+
+
+@pytest.fixture(autouse=True)
+def cleanup_fixture(should_do_global_cleanup_after_test: bool):
+    yield
+    if should_do_global_cleanup_after_test:
+        cleanup()
+
+
+@pytest.fixture
+def example_prompts() -> List[str]:
+    prompts = []
+    for filename in _TEST_PROMPTS:
+        prompts += _read_prompts(filename)
+    return prompts
+
+
+@pytest.fixture
+def example_long_prompts() -> List[str]:
+    prompts = []
+    for filename in _LONG_PROMPTS:
+        prompts += _read_prompts(filename)
+    return prompts
+
+
+@pytest.fixture(scope="session")
+def image_assets() -> _ImageAssets:
+    return IMAGE_ASSETS
+
+
+_STR_DTYPE_TO_TORCH_DTYPE = {
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+    "float": torch.float,
+}
+
+_T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding)
+
+
+class HfRunner:
+    def wrap_device(self, input: _T) -> _T:
+        if not is_cpu():
+            return input.to("cuda")
+        else:
+            return input.to("cpu")
+
+    def __init__(
+        self,
+        model_name: str,
+        dtype: str = "half",
+        *,
+        model_kwargs: Optional[Dict[str, Any]] = None,
+        is_embedding_model: bool = False,
+        is_vision_model: bool = False,
+        is_sparseml_model: bool = False,
+    ) -> None:
+        assert dtype in _STR_DTYPE_TO_TORCH_DTYPE
+        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
+
+        self.model_name = model_name
+
+        if is_embedding_model:
+            # Lazy init required for AMD CI
+            from sentence_transformers import SentenceTransformer
+
+            self.model = self.wrap_device(
+                SentenceTransformer(
+                    model_name,
+                    device="cpu",
+                ).to(dtype=torch_dtype)
+            )
+        else:
+            if is_vision_model:
+                auto_cls = AutoModelForVision2Seq
+            elif is_sparseml_model:
+                from sparseml.transformers import SparseAutoModelForCausalLM
+
+                auto_cls = SparseAutoModelForCausalLM
+            else:
+                auto_cls = AutoModelForCausalLM
+
+            model_kwargs = model_kwargs if model_kwargs is not None else {}
+            self.model = self.wrap_device(
+                auto_cls.from_pretrained(
+                    model_name,
+                    torch_dtype=torch_dtype,
+                    trust_remote_code=True,
+                    **model_kwargs,
+                )
+            )
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            model_name,
+            torch_dtype=torch_dtype,
+            trust_remote_code=True,
+        )
+
+        try:
+            # don't put this import at the top level
+            # it will call torch.cuda.device_count()
+            from transformers import AutoProcessor  # noqa: F401
+
+            self.processor = AutoProcessor.from_pretrained(
+                model_name,
+                torch_dtype=torch_dtype,
+                trust_remote_code=True,
+            )
+        except Exception:
+            logger.warning(
+                "Unable to auto-load processor from HuggingFace for model %s. Using tokenizer instead.",
+                model_name,
+            )
+            self.processor = self.tokenizer
+
+    def generate(
+        self,
+        prompts: List[str],
+        images: Optional[List[Image.Image]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[List[List[int]], List[str]]]:
+        if images:
+            assert len(prompts) == len(images)
+
+        outputs: List[Tuple[List[List[int]], List[str]]] = []
+        for i, prompt in enumerate(prompts):
+            processor_kwargs: Dict[str, Any] = {
+                "text": prompt,
+                "return_tensors": "pt",
+            }
+            if images is not None and images[i] is not None:
+                processor_kwargs["images"] = images[i]
+
+            inputs = self.processor(**processor_kwargs)
+
+            output_ids = self.model.generate(
+                **self.wrap_device(inputs),
+                use_cache=True,
+                **kwargs,
+            )
+            output_str = self.processor.batch_decode(
+                output_ids,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False,
+            )
+            output_ids = output_ids.cpu().tolist()
+            outputs.append((output_ids, output_str))
+        return outputs
+
+    def generate_greedy(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+        images: Optional[List[Image.Image]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[List[int], str]]:
+        outputs = self.generate(
+            prompts,
+            do_sample=False,
+            max_new_tokens=max_tokens,
+            images=images,
+            **kwargs,
+        )
+
+        return [(output_ids[0], output_str[0]) for output_ids, output_str in outputs]
+
+    def generate_beam_search(
+        self,
+        prompts: List[str],
+        beam_width: int,
+        max_tokens: int,
+    ) -> List[Tuple[List[List[int]], List[str]]]:
+        outputs = self.generate(
+            prompts,
+            do_sample=False,
+            max_new_tokens=max_tokens,
+            num_beams=beam_width,
+            num_return_sequences=beam_width,
+        )
+        for i in range(len(outputs)):
+            output_ids, output_str = outputs[i]
+            for j in range(len(output_ids)):
+                output_ids[j] = [x for x in output_ids[j] if x != self.tokenizer.pad_token_id]
+            outputs[i] = (output_ids, output_str)
+        return outputs
+
+    def generate_greedy_logprobs(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+        images: Optional[List[Image.Image]] = None,
+        **kwargs: Any,
+    ) -> List[List[torch.Tensor]]:
+        all_logprobs: List[List[torch.Tensor]] = []
+        for i, prompt in enumerate(prompts):
+            processor_kwargs: Dict[str, Any] = {
+                "text": prompt,
+                "return_tensors": "pt",
+            }
+            if images is not None and images[i] is not None:
+                processor_kwargs["images"] = images[i]
+
+            inputs = self.processor(**processor_kwargs)
+
+            output = self.model.generate(
+                **self.wrap_device(inputs),
+                use_cache=True,
+                do_sample=False,
+                max_new_tokens=max_tokens,
+                output_hidden_states=True,
+                return_dict_in_generate=True,
+                **kwargs,
+            )
+            seq_logprobs: List[torch.Tensor] = []
+            for hidden_states in output.hidden_states:
+                last_hidden_states = hidden_states[-1][0]
+                logits = torch.matmul(
+                    last_hidden_states,
+                    self.model.get_output_embeddings().weight.t(),
+                )
+                if self.model.get_output_embeddings().bias is not None:
+                    logits += self.model.get_output_embeddings().bias.unsqueeze(0)
+                logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
+                seq_logprobs.append(logprobs)
+            all_logprobs.append(seq_logprobs)
+        return all_logprobs
+
+    def generate_greedy_logprobs_limit(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+        num_logprobs: int,
+        images: Optional[List[Image.Image]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[List[int], str, List[Dict[int, float]]]]:
+        all_logprobs: List[List[Dict[int, float]]] = []
+        all_output_ids: List[List[int]] = []
+        all_output_strs: List[str] = []
+
+        for i, prompt in enumerate(prompts):
+            processor_kwargs: Dict[str, Any] = {
+                "text": prompt,
+                "return_tensors": "pt",
+            }
+            if images is not None and images[i] is not None:
+                processor_kwargs["images"] = images[i]
+
+            inputs = self.processor(**processor_kwargs)
+            input_ids = inputs.input_ids
+
+            output = self.model.generate(
+                **self.wrap_device(inputs),
+                use_cache=True,
+                do_sample=False,
+                max_new_tokens=max_tokens,
+                output_hidden_states=True,
+                return_dict_in_generate=True,
+                **kwargs,
+            )
+
+            seq_logprobs: List[torch.Tensor] = []
+            for _, hidden_states in enumerate(output.hidden_states):
+                last_hidden_states = hidden_states[-1][0]
+                logits = torch.matmul(
+                    last_hidden_states,
+                    self.model.get_output_embeddings().weight.t(),
+                )
+                if getattr(self.model.get_output_embeddings(), "bias", None) is not None:
+                    logits += self.model.get_output_embeddings().bias.unsqueeze(0)
+                logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
+                seq_logprobs.append(logprobs)
+
+            # convert to dict
+            seq_logprobs_lst: List[Dict[int, float]] = []
+            for tok_idx, tok_logprobs in enumerate(seq_logprobs):
+                # drop prompt logprobs
+                if tok_idx == 0:
+                    tok_logprobs = tok_logprobs[-1, :].reshape(1, -1)
+                topk = tok_logprobs.topk(num_logprobs)
+
+                tok_logprobs_dct = {}
+                for token_id, logprob in zip(topk.indices[0], topk.values[0]):
+                    tok_logprobs_dct[token_id.item()] = logprob.item()
+
+                seq_logprobs_lst.append(tok_logprobs_dct)
+
+            all_logprobs.append(seq_logprobs_lst)
+            seq_ids = output.sequences[0]
+            output_len = seq_ids.shape[0] - input_ids.shape[1]
+            output_ids = seq_ids[-output_len:]
+            all_output_ids.append(output_ids.tolist())
+            all_output_strs.append(self.tokenizer.decode(output_ids))
+
+        outputs = zip(all_output_ids, all_output_strs, all_logprobs)
+        return [(output_ids, output_str, output_logprobs) for output_ids, output_str, output_logprobs in outputs]
+
+    def encode(self, prompts: List[str]) -> List[List[torch.Tensor]]:
+        return self.model.encode(prompts)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        del self.model
+        cleanup()
+
+
+@pytest.fixture(scope="session")
+def hf_runner():
+    return HfRunner
+
+
+class VllmRunner:
+    def __init__(
+        self,
+        model_name: str,
+        tokenizer_name: Optional[str] = None,
+        # Use smaller max model length, otherwise bigger model cannot run due
+        # to kv cache size limit.
+        max_model_len: int = 1024,
+        dtype: str = "half",
+        disable_log_stats: bool = True,
+        tensor_parallel_size: int = 1,
+        block_size: int = 16,
+        enable_chunked_prefill: bool = False,
+        swap_space: int = 4,
+        enforce_eager: bool = False,
+        **kwargs,
+    ) -> None:
+        self.model = LLM(
+            model=model_name,
+            tokenizer=tokenizer_name,
+            trust_remote_code=True,
+            dtype=dtype,
+            swap_space=swap_space,
+            enforce_eager=enforce_eager,
+            disable_log_stats=disable_log_stats,
+            tensor_parallel_size=tensor_parallel_size,
+            max_model_len=max_model_len,
+            block_size=block_size,
+            enable_chunked_prefill=enable_chunked_prefill,
+            **kwargs,
+        )
+
+    def generate(
+        self,
+        prompts: List[str],
+        sampling_params: SamplingParams,
+        images: Optional[List[Image.Image]] = None,
+    ) -> List[Tuple[List[List[int]], List[str]]]:
+        if images is not None:
+            assert len(prompts) == len(images)
+
+        inputs = [TextPrompt(prompt=prompt) for prompt in prompts]
+        if images is not None:
+            for i, image in enumerate(images):
+                inputs[i]["multi_modal_data"] = {"image": image}
+
+        req_outputs = self.model.generate(inputs, sampling_params=sampling_params)
+
+        outputs: List[Tuple[List[List[int]], List[str]]] = []
+        for req_output in req_outputs:
+            prompt_str = req_output.prompt
+            prompt_ids = req_output.prompt_token_ids
+            req_sample_output_ids: List[List[int]] = []
+            req_sample_output_strs: List[str] = []
+            for sample in req_output.outputs:
+                output_str = sample.text
+                output_ids = list(sample.token_ids)
+                req_sample_output_ids.append(prompt_ids + output_ids)
+                req_sample_output_strs.append(prompt_str + output_str)
+            outputs.append((req_sample_output_ids, req_sample_output_strs))
+        return outputs
+
+    def generate_w_logprobs(
+        self,
+        prompts: List[str],
+        sampling_params: SamplingParams,
+        images: Optional[List[Image.Image]] = None,
+    ) -> List[Tuple[List[int], str, Optional[SampleLogprobs]]]:
+        assert sampling_params.logprobs is not None
+
+        if images is not None:
+            assert len(prompts) == len(images)
+
+        inputs = [TextPrompt(prompt=prompt) for prompt in prompts]
+        if images is not None:
+            for i, image in enumerate(images):
+                inputs[i]["multi_modal_data"] = {"image": image}
+
+        req_outputs = self.model.generate(inputs, sampling_params=sampling_params)
+        outputs: List[Tuple[List[int], str, Optional[SampleLogprobs]]] = []
+        for req_output in req_outputs:
+            for sample in req_output.outputs:
+                output_str = sample.text
+                output_ids = sample.token_ids
+                output_logprobs = sample.logprobs
+            outputs.append((output_ids, output_str, output_logprobs))
+        return outputs
+
+    def generate_greedy(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+        images: Optional[List[Image.Image]] = None,
+    ) -> List[Tuple[List[int], str]]:
+        greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
+        outputs = self.generate(prompts, greedy_params, images=images)
+        return [(output_ids[0], output_str[0]) for output_ids, output_str in outputs]
+
+    def generate_greedy_logprobs(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+        num_logprobs: int,
+        images: Optional[List[Image.Image]] = None,
+    ) -> List[Tuple[List[int], str, Optional[SampleLogprobs]]]:
+        greedy_logprobs_params = SamplingParams(temperature=0.0, max_tokens=max_tokens, logprobs=num_logprobs)
+        outputs = self.generate_w_logprobs(prompts, greedy_logprobs_params, images=images)
+
+        return [(output_ids, output_str, output_logprobs) for output_ids, output_str, output_logprobs in outputs]
+
+    def generate_beam_search(
+        self,
+        prompts: List[str],
+        beam_width: int,
+        max_tokens: int,
+    ) -> List[Tuple[List[List[int]], List[str]]]:
+        beam_search_params = SamplingParams(
+            n=beam_width,
+            use_beam_search=True,
+            temperature=0.0,
+            max_tokens=max_tokens,
+        )
+        outputs = self.generate(prompts, beam_search_params)
+        return outputs
+
+    def encode(self, prompts: List[str]) -> List[List[float]]:
+        req_outputs = self.model.encode(prompts)
+        outputs = []
+        for req_output in req_outputs:
+            embedding = req_output.outputs.embedding
+            outputs.append(embedding)
+        return outputs
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        del self.model
+        cleanup()
+
+
+@pytest.fixture(scope="session")
+def vllm_runner():
+    return VllmRunner
+
+
+def get_tokenizer_pool_config(tokenizer_group_type):
+    if tokenizer_group_type is None:
+        return None
+    if tokenizer_group_type == "ray":
+        return TokenizerPoolConfig(pool_size=1, pool_type="ray", extra_config={})
+    raise ValueError(f"Unknown tokenizer_group_type: {tokenizer_group_type}")
+
+
+@pytest.fixture()
+def temporary_enable_log_propagate():
+    import logging
+
+    logger = logging.getLogger("vllm")
+    logger.propagate = True
+    yield
+    logger.propagate = False
+
+
+@pytest.fixture()
+def caplog_vllm(temporary_enable_log_propagate, caplog):
+    # To capture vllm log, we should enable propagate=True temporarily
+    # because caplog depends on logs propagated to the root logger.
+    yield caplog
+
+
+@pytest.fixture(scope="session")
+def num_gpus_available():
+    """Get number of GPUs without initializing the CUDA context
+    in current process."""
+
+    return cuda_device_count_stateless()

tilelang/original/examples/bitnet-1.58b/vllm_workspace/inference_with_compress_format.py

+"""Compare the outputs of a GPTQ model to a Marlin model.
+
+Note: GPTQ and Marlin do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+Marlin/GPTQ models are in the top 3 selections of each other.
+
+Note: Marlin internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for Marlin. As a result, we re-run the test
+up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_marlin.py`.
+"""
+
+from conftest import VllmRunner
+import os
+import argparse
+
+# get the path of the current file
+current_file_path = os.path.realpath(__file__)
+current_dir = os.path.dirname(current_file_path)
+
+ckpt_path = os.path.join(current_dir, "../models/ckpt_bitnet_b1_58-3B_bitblas")
+parser = argparse.ArgumentParser(description="Inference with BitNet")
+parser.add_argument(
+    "--ckpt_path",
+    type=str,
+    default=ckpt_path,
+    help="Path to the checkpoint",
+)
+
+args = parser.parse_args()
+
+ckpt_path = args.ckpt_path
+with VllmRunner(
+    ckpt_path,
+    dtype="half",
+    quantization="bitblas",
+    # set enforce_eager = False to enable cuda graph
+    # set enforce_eager = True to disable cuda graph
+    enforce_eager=False,
+) as bitnet_model:
+    bitbnet_outputs = bitnet_model.generate_greedy(["Hi, tell me about microsoft?"], max_tokens=1024)
+    print("bitnet inference:")
+    print(bitbnet_outputs[0][0])
+    print(bitbnet_outputs[0][1])

tilelang/original/examples/bitnet-1.58b/vllm_workspace/inference_with_native_format.py

+"""Compare the outputs of a GPTQ model to a Marlin model.
+
+Note: GPTQ and Marlin do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+Marlin/GPTQ models are in the top 3 selections of each other.
+
+Note: Marlin internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for Marlin. As a result, we re-run the test
+up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_marlin.py`.
+"""
+
+from conftest import VllmRunner
+import os
+import argparse
+
+# get the path of the current file
+current_file_path = os.path.realpath(__file__)
+current_dir = os.path.dirname(current_file_path)
+ckpt_path = os.path.join(current_dir, "../models/ckpt_bitnet_b1_58-3B")
+
+parser = argparse.ArgumentParser(description="Inference with BitNet")
+parser.add_argument(
+    "--ckpt_path",
+    type=str,
+    default=ckpt_path,
+    help="Path to the checkpoint",
+)
+
+args = parser.parse_args()
+
+ckpt_path = args.ckpt_path
+
+with VllmRunner(
+    ckpt_path,
+    dtype="half",
+    quantization="bitnet_bitblas",
+    gpu_memory_utilization=0.5,
+    # set enforce_eager = False to enable cuda graph
+    # set enforce_eager = True to disable cuda graph
+    enforce_eager=False,
+) as bitnet_model:
+    bitbnet_outputs = bitnet_model.generate_greedy(["Hi, tell me about microsoft?"], max_tokens=128)
+    print("bitnet inference output:")
+    print(bitbnet_outputs[0][0])
+    print(bitbnet_outputs[0][1])

tilelang/original/examples/bitnet-1.58b/vllm_workspace/utils.py

+from typing import Dict, List, Tuple
+
+TokensText = Tuple[List[int], str]
+
+
+def check_outputs_equal(outputs_0_lst: List[TokensText], outputs_1_lst: List[TokensText], name_0: str, name_1: str):
+    """
+    Compare the two sequences generated by different models,
+    which should be equal.
+    """
+    assert len(outputs_0_lst) == len(outputs_1_lst)
+
+    for prompt_idx, (outputs_0, outputs_1) in enumerate(zip(outputs_0_lst, outputs_1_lst)):
+        output_ids_0, output_str_0 = outputs_0
+        output_ids_1, output_str_1 = outputs_1
+
+        assert output_str_0 == output_str_1, f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}"
+        assert output_ids_0 == output_ids_1, f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}"
+
+
+TokensTextLogprobs = Tuple[List[int], str, List[Dict[int, float]]]
+
+
+def check_logprobs_close(outputs_0_lst: List[TokensTextLogprobs], outputs_1_lst: List[TokensTextLogprobs], name_0: str, name_1: str):
+    """
+    Compare the logprobs of two sequences generated by different models,
+    which should be similar but not necessarily equal.
+    """
+    assert len(outputs_0_lst) == len(outputs_1_lst)
+
+    # Loop through responses to each prompt.
+    for prompt_idx, (outputs_0, outputs_1) in enumerate(zip(outputs_0_lst, outputs_1_lst)):
+        output_ids_0, output_str_0, logprobs_0 = outputs_0
+        output_ids_1, output_str_1, logprobs_1 = outputs_1
+
+        # Loop through generated tokens.
+        for idx, (output_id_0, output_id_1) in enumerate(zip(output_ids_0, output_ids_1)):
+            # If generated tokens don't match, then
+            if output_id_0 != output_id_1:
+                # Each predicted token must be in top N logprobs of the other
+                assert output_id_0 in logprobs_1[idx], f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}"
+                assert output_id_1 in logprobs_0[idx], f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}"
+
+                # Break out since sequences will now diverge.
+                break

tilelang/original/examples/blocksparse_attention/README.md

+# Block-Sparse Flash-Attention
+
+Tilelang implementation of block-sparse flash-attention kernels. 
+
+The kernels have been used in [Rectified Sparse Attention](https://arxiv.org/abs/2506.04108) and [SeerAttention-R](https://arxiv.org/abs/2506.08889). 
+

tilelang/original/examples/blocksparse_attention/block_sparse_attn_triton.py

+# ruff: noqa: E712
+import math
+import torch
+
+import triton
+import triton.language as tl
+import torch.nn.functional as F
+
+
+def is_hip():
+    return triton.runtime.driver.active.get_current_target().backend == "hip"
+
+
+def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False):
+    bsz, num_head, downsample_len, _ = x.shape
+    # N_CTX = downsample_len * BLOCK
+    sparse_index = torch.topk(x, topk, dim=-1).indices
+    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device)
+    dense_mask.scatter_(-1, sparse_index, True)
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False):
+    dense_mask = x > threshold
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+@triton.jit
+def _fwd_kernel_inner(
+    acc,
+    l_i,
+    m_i,
+    q,
+    k_block_col_idx,
+    block_mask_ptr,
+    k_ptrs,
+    v_ptrs,
+    offs_m,
+    offs_n,
+    stride_kt,
+    stride_vt,
+    stride_bmask_n,
+    sm_scale,
+    seqlen_k,
+    past_len,
+    LAST_K_BLOCK: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    mask_val = tl.load(block_mask_ptr + k_block_col_idx * stride_bmask_n)
+    # print
+
+    if mask_val == True:
+        start_n = k_block_col_idx * BLOCK_N
+        # -- compute qk ----
+
+        k = tl.load(k_ptrs + start_n * stride_kt)
+
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        qk += tl.dot(q, k)
+
+        qk *= sm_scale
+
+        # the following is needed only when LAST_K_BLOCK or BLOCK_M < BLOCK_N
+        if LAST_K_BLOCK:
+            qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, float("-inf"))
+
+        m_ij = tl.maximum(m_i, tl.max(qk, 1))
+        qk -= m_ij[:, None]
+        p = tl.exp(qk)
+        l_ij = tl.sum(p, 1)
+        alpha = tl.exp(m_i - m_ij)
+        l_i = l_i * alpha + l_ij
+        acc = acc * alpha[:, None]
+
+        # update acc
+        v = tl.load(v_ptrs + start_n * stride_vt)
+
+        p = p.to(v.type.element_ty)
+
+        acc += tl.dot(p, v)
+        # update m_i and l_i
+        m_i = m_ij
+    return acc, l_i, m_i
+
+
+@triton.jit
+def _fwd_kernel(
+    Q,
+    K,
+    V,
+    sm_scale,
+    block_mask_ptr,
+    Out,
+    stride_qz,
+    stride_qh,
+    stride_qm,
+    stride_qd,
+    stride_kz,
+    stride_kh,
+    stride_kn,
+    stride_kd,
+    stride_vz,
+    stride_vh,
+    stride_vn,
+    stride_vd,
+    stride_bmz,
+    stride_bmh,
+    stride_bmm,
+    stride_bmn,
+    stride_oz,
+    stride_oh,
+    stride_om,
+    stride_od,
+    H,
+    N_CTX,
+    PAST_LEN,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+):
+    Q_LEN = N_CTX - PAST_LEN
+    start_m = tl.program_id(0)
+    off_hz = tl.program_id(1)
+    off_h = off_hz % H
+    off_z = off_hz // H
+    Q += off_z * stride_qz + off_h * stride_qh
+    K += off_z * stride_kz + off_h * stride_kh
+    V += off_z * stride_vz + off_h * stride_vh
+    block_mask_ptr += off_z * stride_bmz + off_h * stride_bmh
+
+    # initialize offsets
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, BLOCK_DMODEL)
+    off_q = offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qd
+    # off_k = offs_n[:, None] * stride_kn + offs_d[None, :] * stride_kd
+    off_k = offs_n[None, :] * stride_kn + offs_d[:, None] * stride_kd
+    off_v = offs_n[:, None] * stride_vn + offs_d[None, :] * stride_vd
+    # Initialize pointers to Q, K, V
+    q_ptrs = Q + off_q
+    k_ptrs = K + off_k
+    v_ptrs = V + off_v
+    mask_ptrs = block_mask_ptr + start_m * stride_bmm
+
+    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+
+    q = tl.load(q_ptrs, mask=offs_m[:, None] < Q_LEN)
+
+    k_block_start = 0
+    k_block_end = tl.cdiv((start_m + 1) * BLOCK_M, BLOCK_N)
+
+    # loop over k, v and update accumulator
+    for col_idx in range(k_block_start, k_block_end):
+        acc, l_i, m_i = _fwd_kernel_inner(
+            acc,
+            l_i,
+            m_i,
+            q,
+            col_idx,
+            mask_ptrs,
+            k_ptrs,
+            v_ptrs,
+            offs_m,
+            offs_n,
+            stride_kn,
+            stride_vn,
+            stride_bmn,
+            sm_scale,
+            N_CTX,
+            PAST_LEN,
+            col_idx == k_block_end - 1,
+            BLOCK_M,
+            BLOCK_N,
+        )
+
+    m_i += tl.math.log(l_i)
+    l_recip = 1 / l_i[:, None]
+    acc = acc * l_recip
+    acc = acc.to(Out.dtype.element_ty)
+
+    off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :] * stride_od
+    out_ptrs = Out + off_o
+    tl.store(out_ptrs, acc, mask=offs_m[:, None] < N_CTX)
+
+
+def _forward(ctx, q, k, v, block_sparse_mask, sm_scale, BLOCK_M=64, BLOCK_N=64, num_warps=None, num_stages=1, out=None):
+    assert q.shape[-1] == k.shape[-1] == v.shape[-1]
+    assert k.shape[2] == v.shape[2]
+    o = out if out is not None else torch.empty_like(q).contiguous()
+    grid = (triton.cdiv(q.shape[2], BLOCK_M), q.shape[0] * q.shape[1])
+
+    assert q.shape[-1] in [64, 128]
+    BLOCK_DMODEL = q.shape[-1]
+
+    if is_hip():
+        num_warps, num_stages = 8, 1
+    else:
+        num_warps, num_stages = 4, 2
+
+    N_CTX = k.shape[2]
+    PAST_LEN = N_CTX - q.shape[2]
+
+    H = q.shape[1]
+
+    _fwd_kernel[grid](
+        q,
+        k,
+        v,
+        sm_scale,
+        block_sparse_mask,
+        o,
+        *q.stride(),
+        *k.stride(),
+        *v.stride(),
+        *block_sparse_mask.stride(),
+        *o.stride(),
+        H,
+        N_CTX,
+        PAST_LEN,
+        BLOCK_M,
+        BLOCK_N,
+        BLOCK_DMODEL,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+
+    return o
+
+
+class _sparse_attention(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, q, k, v, block_sparse_dense, sm_scale):
+        # shape constraints
+        return _forward(ctx, q, k, v, block_sparse_dense, sm_scale)
+
+    @staticmethod
+    def backward(ctx, do):
+        # No gradient propagation.
+        raise NotImplementedError("It does not support gradient propagation yet")
+        return None, None, None, None, None
+
+
+block_sparse_triton_fn = _sparse_attention.apply
+
+
+def test_topk_sparse_attention():
+    # Config
+    BATCH, N_HEADS, SEQ_LEN, D_HEAD = 1, 1, 256, 64
+    TOPK = 2  # Keep top 8 elements per row
+    BLOCK = 64
+    torch.manual_seed(0)
+
+    # Create inputs
+    q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16)
+    k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16)
+    v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16)
+    sm_scale = 1.0 / (D_HEAD**0.5)
+
+    # Create sparse mask (downsampled to block level)
+    downsample_factor = BLOCK
+    downsample_len = math.ceil(SEQ_LEN / downsample_factor)
+    print("downsample_len", downsample_len)
+
+    x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16)
+    x_ds[:, :, :, 0] = 100
+    print("x_ds.shape", x_ds.shape)
+    block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK)
+    # print("block_mask", block_mask)
+    print("block_mask.shape", block_mask.shape)
+
+    # Run Triton kernel
+    triton_output = block_sparse_triton_fn(q, k, v, block_mask, sm_scale)
+
+    # Compute reference
+    # Expand block mask to full attention matrix
+    full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda"))
+    full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool()
+    full_mask = full_mask & torch.tril(torch.ones_like(full_mask))  # Apply causal
+
+    # PyTorch reference implementation
+    attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale
+    attn = attn.masked_fill(~full_mask, float("-inf"))
+    attn = F.softmax(attn, dim=-1)
+    ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v)
+
+    # print("ref_output", ref_output)
+    # print("triton_output", triton_output)
+
+    # Verify accuracy
+    assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), "Triton output doesn't match reference"
+    print("Pass topk sparse attention test with qlen == klen")
+
+
+def test_topk_sparse_attention_qlt_kl():
+    BATCH, N_HEADS = 2, 4
+    Q_LEN, K_LEN, D_HEAD = 128, 256, 64  # qlen < klen; here, past_len = 256 - 128 = 128.
+    TOPK = 1
+    BLOCK = 64  # block size used in downsampling
+    torch.manual_seed(0)
+
+    # Create inputs.
+    q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16)
+    k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16)
+    v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16)
+    # softmax scale
+    sm_scale = 1.0 / (D_HEAD**0.5)
+
+    downsample_factor = BLOCK
+    print("downsample_factor", downsample_factor)
+    downsample_len = math.ceil(K_LEN / downsample_factor)  # number of blocks along one dimension
+    print("downsample_len", downsample_len)
+    x_ds = torch.randn(BATCH, N_HEADS, downsample_len, downsample_len, device="cuda", dtype=torch.bfloat16)
+    # Force the first column to be high so that the first block is always selected.
+    x_ds[:, :, :, 0] = 100
+    block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK)
+    print("block_mask", block_mask)
+    print("block_mask.shape", block_mask.shape)
+    # Run Triton kernel.
+    triton_output = block_sparse_triton_fn(q, k, v, block_mask, sm_scale)
+
+    past_len = K_LEN - Q_LEN
+
+    attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale
+
+    full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")).bool()
+    full_mask_full = full_mask_full[..., :K_LEN, :K_LEN]
+
+    effective_mask = full_mask_full[..., past_len:K_LEN, :]  # shape: (B, H, Q_LEN, K_LEN)
+
+    i_global = torch.arange(past_len, K_LEN, device=k.device).unsqueeze(1)  # shape: (Q_LEN, 1)
+    j_global = torch.arange(K_LEN, device=k.device).unsqueeze(0)  # shape: (1, K_LEN)
+    causal_mask = j_global <= i_global  # shape: (Q_LEN, K_LEN)
+
+    final_mask = effective_mask & causal_mask  # shape: (B, H, Q_LEN, K_LEN)
+
+    attn = attn.masked_fill(~final_mask, float("-inf"))
+    attn = F.softmax(attn, dim=-1)
+    ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v)
+
+    # Verify accuracy.
+    assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), "Triton output doesn't match reference when qlen < klen"
+
+    print("Pass topk sparse attention test with qlen < klen")
+
+
+def main():
+    test_topk_sparse_attention()
+    test_topk_sparse_attention_qlt_kl()
+
+
+if __name__ == "__main__":
+    main()

tilelang/original/examples/blocksparse_attention/example_tilelang_block_sparse_attn.py

+import math
+import torch
+
+import tilelang
+import tilelang.language as T
+import torch.nn.functional as F
+
+
+def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False):
+    bsz, num_head, downsample_len, _ = x.shape
+    # N_CTX = downsample_len * BLOCK
+    sparse_index = torch.topk(x, topk, dim=-1).indices
+    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device)
+    dense_mask.scatter_(-1, sparse_index, True)
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False):
+    dense_mask = x > threshold
+    if use_dense_for_last_block:
+        dense_mask[:, :, -2:, :] = True
+    dense_mask.tril_()
+    return dense_mask
+
+
+@tilelang.jit(
+    out_idx=[4],
+    pass_configs={
+        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+    },
+)
+def blocksparse_flashattn(batch, heads, seq_len, dim, downsample_len, is_causal):
+    block_M = 64
+    block_N = 64
+    num_stages = 1
+    threads = 128
+    scale = (1.0 / dim) ** 0.5 * 1.44269504  # log2(e)
+    shape = [batch, heads, seq_len, dim]
+    block_mask_shape = [batch, heads, downsample_len, downsample_len]
+
+    dtype = T.float16
+    accum_dtype = T.float32
+    block_mask_dtype = T.bool
+
+    def kernel_func(block_M, block_N, num_stages, threads):
+        @T.macro
+        def MMA0(
+            K: T.Tensor(shape, dtype),
+            Q_shared: T.SharedBuffer([block_M, dim], dtype),
+            K_shared: T.SharedBuffer([block_N, dim], dtype),
+            acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
+            k: T.int32,
+            bx: T.int32,
+            by: T.int32,
+            bz: T.int32,
+        ):
+            T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared)
+            if is_causal:
+                for i, j in T.Parallel(block_M, block_N):
+                    acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype))
+            else:
+                T.clear(acc_s)
+            T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+
+        @T.macro
+        def MMA1(
+            V: T.Tensor(shape, dtype),
+            V_shared: T.SharedBuffer([block_M, dim], dtype),
+            acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
+            acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+            k: T.int32,
+            by: T.int32,
+            bz: T.int32,
+        ):
+            T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared)
+            T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+
+        @T.macro
+        def Softmax(
+            acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
+            acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
+            scores_max: T.FragmentBuffer([block_M], accum_dtype),
+            scores_max_prev: T.FragmentBuffer([block_M], accum_dtype),
+            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
+            scores_sum: T.FragmentBuffer([block_M], accum_dtype),
+            logsum: T.FragmentBuffer([block_M], accum_dtype),
+        ):
+            T.copy(scores_max, scores_max_prev)
+            T.fill(scores_max, -T.infinity(accum_dtype))
+            T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+            # To do causal softmax, we need to set the scores_max to 0 if it is -inf
+            # This process is called Check_inf in FlashAttention3 code, and it only need to be done
+            # in the first ceil_div(kBlockM, kBlockN) steps.
+            # for i in T.Parallel(block_M):
+            #     scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i])
+            for i in T.Parallel(block_M):
+                scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+            for i, j in T.Parallel(block_M, block_N):
+                # Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
+                # max * log_2(e)) This allows the compiler to use the ffma
+                # instruction instead of fadd and fmul separately.
+                acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+            T.reduce_sum(acc_s, scores_sum, dim=1)
+            for i in T.Parallel(block_M):
+                logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+            T.copy(acc_s, acc_s_cast)
+
+        @T.macro
+        def Rescale(
+            acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
+        ):
+            for i, j in T.Parallel(block_M, dim):
+                acc_o[i, j] *= scores_scale[i]
+
+        @T.prim_func
+        def blocksparse_flashattn(
+            Q: T.Tensor(shape, dtype),
+            K: T.Tensor(shape, dtype),
+            V: T.Tensor(shape, dtype),
+            BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype),
+            Output: T.Tensor(shape, dtype),
+        ):
+            with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz):
+                Q_shared = T.alloc_shared([block_M, dim], dtype)
+                K_shared = T.alloc_shared([block_N, dim], dtype)
+                V_shared = T.alloc_shared([block_N, dim], dtype)
+                O_shared = T.alloc_shared([block_M, dim], dtype)
+                acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
+                acc_s_cast = T.alloc_fragment([block_M, block_N], dtype)
+                acc_o = T.alloc_fragment([block_M, dim], accum_dtype)
+                scores_max = T.alloc_fragment([block_M], accum_dtype)
+                scores_max_prev = T.alloc_fragment([block_M], accum_dtype)
+                scores_scale = T.alloc_fragment([block_M], accum_dtype)
+                scores_sum = T.alloc_fragment([block_M], accum_dtype)
+                logsum = T.alloc_fragment([block_M], accum_dtype)
+                block_mask = T.alloc_local([downsample_len], block_mask_dtype)
+
+                T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared)
+                T.fill(acc_o, 0)
+                T.fill(logsum, 0)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+
+                for vj in T.serial(downsample_len):
+                    block_mask[vj] = BlockSparseMask[bz, by, bx, vj]
+
+                loop_range = (
+                    T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)
+                )
+
+                for k in T.Pipelined(loop_range, num_stages=num_stages):
+                    if block_mask[k] != 0:
+                        MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz)
+                        Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, logsum)
+                        Rescale(acc_o, scores_scale)
+                        MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz)
+                for i, j in T.Parallel(block_M, dim):
+                    acc_o[i, j] /= logsum[i]
+                T.copy(acc_o, O_shared)
+                T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :])
+
+        return blocksparse_flashattn
+
+    return kernel_func(block_M, block_N, num_stages, threads)
+
+
+def test_topk_sparse_attention():
+    # Config
+    BATCH, N_HEADS, SEQ_LEN, D_HEAD = 1, 1, 256, 64
+    TOPK = 2  # Keep top 8 elements per row
+    BLOCK = 64
+    torch.manual_seed(0)
+
+    # Create inputs
+    q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16)
+    k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16)
+    v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16)
+
+    sm_scale = 1.0 / (D_HEAD**0.5)
+
+    # Create sparse mask (downsampled to block level)
+    downsample_factor = BLOCK
+    downsample_len = math.ceil(SEQ_LEN / downsample_factor)
+    x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16)
+    x_ds[:, :, :, 0] = 100
+    block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK)
+
+    # Run tilelang kernel
+    kernel = blocksparse_flashattn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, downsample_len, is_causal=True)
+
+    tilelang_output = kernel(q, k, v, block_mask)
+
+    # Compute reference
+    # Expand block mask to full attention matrix
+    full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda"))
+    full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool()
+    full_mask = full_mask & torch.tril(torch.ones_like(full_mask))  # Apply causal
+
+    # PyTorch reference implementation
+    attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale
+    attn = attn.masked_fill(~full_mask, float("-inf"))
+    attn = F.softmax(attn, dim=-1)
+    ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v)
+
+    print("ref_output", ref_output)
+    print("tilelang_output", tilelang_output)
+
+    # Verify accuracy
+    torch.testing.assert_close(tilelang_output, ref_output, atol=1e-2, rtol=1e-2)
+    print("Pass topk sparse attention test with qlen == klen")
+
+
+def main():
+    test_topk_sparse_attention()
+
+
+if __name__ == "__main__":
+    main()

tilelang/original/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_paged.py

+# ruff: noqa
+import torch
+import torch.nn.functional as F
+import tilelang
+from tilelang.autotuner import *
+import tilelang.language as T
+from einops import rearrange, einsum
+import argparse
+import time
+import math
+
+from heuristic import num_splits_heuristic
+
+
+def flashattn(batch, heads, heads_kv, dim, dim_v):
+    scale = (1.0 / dim) ** 0.5 * 1.44269504  # log2(e)
+    dtype = T.float16
+    accum_dtype = T.float32
+    kv_group_num = heads // heads_kv
+
+    @tilelang.jit(
+        out_idx=[-1],
+        pass_configs={
+            tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+        },
+    )
+    def kernel_func(
+        block_N, block_H, page_block_size, num_split, num_stages, threads, num_pages, max_num_blocks_per_seq, max_selected_blocks
+    ):
+        shape_q = [batch, heads, dim]
+        shape_k = [num_pages, page_block_size, heads_kv, dim]
+        shape_v = [num_pages, page_block_size, heads_kv, dim_v]
+        shape_indices = [batch, heads_kv, max_selected_blocks]
+        shape_block_table = [batch, max_num_blocks_per_seq]
+        shape_o = [batch, heads, dim_v]
+        part_shape = [batch, heads, num_split, dim_v]
+        valid_block_H = min(block_H, kv_group_num)
+        assert block_N <= page_block_size and page_block_size % block_N == 0
+        block_ratio = page_block_size // block_N
+
+        @T.macro
+        def flash_attn_split(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            block_indices: T.Tensor(shape_indices, T.int32),
+            cache_seqlens: T.Tensor([batch], T.int32),
+            block_table: T.Tensor(shape_block_table, T.int32),
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+        ):
+            with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz):
+                Q_shared = T.alloc_shared([block_H, dim], dtype)
+                K_shared = T.alloc_shared([block_N, dim], dtype)
+                V_shared = T.alloc_shared([block_N, dim_v], dtype)
+                acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+                acc_s_cast = T.alloc_fragment([block_H, block_N], dtype)
+                acc_o = T.alloc_fragment([block_H, dim_v], accum_dtype)
+
+                scores_max = T.alloc_fragment([block_H], accum_dtype)
+                scores_max_prev = T.alloc_fragment([block_H], accum_dtype)
+                scores_scale = T.alloc_fragment([block_H], accum_dtype)
+                scores_sum = T.alloc_fragment([block_H], accum_dtype)
+                logsum = T.alloc_fragment([block_H], accum_dtype)
+                has_valid_block = T.alloc_var("bool")
+
+                bid = bx
+                hid = by
+                sid = bz
+                cur_kv_head = hid // (kv_group_num // valid_block_H)
+
+                T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared)
+                T.fill(acc_o, 0)
+                T.fill(logsum, 0)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+
+                num_blocks = max_selected_blocks
+                blocks_per_split = T.floordiv(num_blocks, num_split)
+                remaining_blocks = T.floormod(num_blocks, num_split)
+                loop_range = blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0)
+                start = blocks_per_split * sid + T.min(sid, remaining_blocks)
+                has_valid_block = False
+                for k in T.Pipelined(loop_range, num_stages=num_stages):
+                    logical_block_idx = block_indices[bid, cur_kv_head, start + k]
+                    if logical_block_idx >= 0:
+                        has_valid_block = True
+                        block_table_idx = T.floordiv(logical_block_idx, block_ratio)
+                        block_tile_idx = T.floormod(logical_block_idx, block_ratio)
+                        physical_block_idx = block_table[bid, block_table_idx]
+                        T.copy(K[physical_block_idx, block_tile_idx * block_N : (block_tile_idx + 1) * block_N, cur_kv_head, :], K_shared)
+                        T.clear(acc_s)
+                        T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+                        if k == 0:  # assume block_indices is sorted in reverse order, otherwise, remove this if condition
+                            for i, j in T.Parallel(block_H, block_N):
+                                acc_s[i, j] = T.if_then_else(
+                                    logical_block_idx * block_N + j >= cache_seqlens[bid], -T.infinity(accum_dtype), acc_s[i, j]
+                                )
+                        T.copy(scores_max, scores_max_prev)
+                        T.fill(scores_max, -T.infinity(accum_dtype))
+                        T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                        for i in T.Parallel(block_H):
+                            scores_max[i] = T.max(scores_max[i], scores_max_prev[i])
+                            scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+                        for i, j in T.Parallel(block_H, block_N):
+                            acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+                        T.reduce_sum(acc_s, scores_sum, dim=1)
+                        for i in T.Parallel(block_H):
+                            logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+                        T.copy(acc_s, acc_s_cast)
+                        for i, j in T.Parallel(block_H, dim_v):
+                            acc_o[i, j] *= scores_scale[i]
+                        T.copy(V[physical_block_idx, block_tile_idx * block_N : (block_tile_idx + 1) * block_N, cur_kv_head, :], V_shared)
+                        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+                if has_valid_block:
+                    for i, j in T.Parallel(block_H, dim_v):
+                        acc_o[i, j] /= logsum[i]
+
+                    for i in T.Parallel(block_H):
+                        logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
+
+                for i in T.Parallel(block_H):
+                    if i < valid_block_H:
+                        glse[bid, hid * valid_block_H + i, sid] = logsum[i]
+
+                for i, j in T.Parallel(block_H, dim_v):
+                    if i < valid_block_H:
+                        Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j]
+
+        @T.macro
+        def combine(
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+            Output: T.Tensor(shape_o, dtype),
+        ):
+            with T.Kernel(heads, batch, threads=128) as (by, bz):
+                po_local = T.alloc_fragment([dim_v], accum_dtype)
+                o_accum_local = T.alloc_fragment([dim_v], accum_dtype)
+                lse_local_split = T.alloc_local([1], accum_dtype)
+                lse_logsum_local = T.alloc_local([1], accum_dtype)
+                lse_max_local = T.alloc_local([1], accum_dtype)
+                scale_local = T.alloc_local([1], accum_dtype)
+                max_split = T.alloc_local([1], T.int32)
+
+                T.annotate_layout(
+                    {
+                        lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i),
+                    }
+                )
+
+                T.clear(lse_logsum_local)
+                T.clear(o_accum_local)
+                lse_max_local[0] = -T.infinity(accum_dtype)
+                for k in T.serial(num_split):
+                    lse_local_split[0] = glse[bz, by, k]
+                    if lse_local_split[0] != 0:
+                        max_split[0] = k
+                        lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k])
+
+                for k in T.Pipelined(num_split, num_stages=1):
+                    if k <= max_split[0]:
+                        lse_local_split[0] = glse[bz, by, k]
+                        lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0])
+                lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0]
+                for k in T.serial(num_split):
+                    if k <= max_split[0]:
+                        for i in T.Parallel(dim_v):
+                            po_local[i] = Output_partial[bz, by, k, i]
+                        lse_local_split[0] = glse[bz, by, k]
+                        scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0])
+                        for i in T.Parallel(dim_v):
+                            o_accum_local[i] += po_local[i] * scale_local[0]
+                for i in T.Parallel(dim_v):
+                    Output[bz, by, i] = o_accum_local[i]
+
+        @T.prim_func
+        def main(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            block_indices: T.Tensor(shape_indices, T.int32),
+            cache_seqlens: T.Tensor([batch], T.int32),
+            block_table: T.Tensor(shape_block_table, T.int32),
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+            Output: T.Tensor(shape_o, dtype),
+        ):
+            flash_attn_split(Q, K, V, block_indices, cache_seqlens, block_table, glse, Output_partial)
+            combine(glse, Output_partial, Output)
+
+        return main
+
+    return kernel_func
+
+
+class SparseFlashAttn(torch.nn.Module):
+    def __init__(self, batch, heads, heads_kv, dim, dim_v, page_block_size, block_N, num_pages):
+        super(SparseFlashAttn, self).__init__()
+        self.batch = batch
+        self.heads = heads
+        self.heads_kv = heads_kv
+        self.dim = dim
+        self.dim_v = dim_v
+        self.block_N = block_N
+        self.page_block_size = page_block_size
+        self.num_pages = num_pages
+        self.block_H = 64
+
+        self.kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
+            block_N=block_N,
+            block_H=self.block_H,
+            page_block_size=page_block_size,
+            num_split=T.dynamic("num_split"),
+            num_stages=2,
+            threads=128,
+            num_pages=num_pages,
+            max_num_blocks_per_seq=T.dynamic("max_num_blocks_per_seq"),
+            max_selected_blocks=T.dynamic("max_selected_blocks"),
+        )
+
+        props = torch.cuda.get_device_properties(torch.device("cuda:0"))
+        self.num_sm = props.multi_processor_count
+
+    def forward(self, query, key, value, block_indices, cache_seqlens, block_table):
+        batch = self.batch
+        heads = self.heads
+        heads_kv = self.heads_kv
+        dim_v = self.dim_v
+        dim = self.dim
+        block_size = self.block_N
+        max_selected_blocks = block_indices.shape[-1]
+
+        # Compute static scheduling parameters
+        num_m_blocks = 1 * (heads // heads_kv + self.block_H - 1) // self.block_H
+        num_n_blocks = max_selected_blocks
+        size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2
+        total_mblocks = batch * heads_kv * num_m_blocks
+
+        num_sm = self.num_sm
+
+        num_split = num_splits_heuristic(
+            total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128
+        )
+
+        glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda")
+        output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda")
+
+        output = self.kernel(
+            query,
+            key,
+            value,
+            block_indices,
+            cache_seqlens,
+            block_table,
+            glse,
+            output_partial,
+        )
+        return output
+
+
+def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_seqlens, block_table, page_block_size, block_size):
+    """
+    Paged version of sparse attention reference implementation.
+
+    Args:
+        query: [batch, heads, dim]
+        key_cache: [num_pages, page_block_size, heads_kv, dim]
+        value_cache: [num_pages, page_block_size, heads_kv, dim]
+        block_indices: [batch, heads_kv, max_selected_blocks] - logical block indices
+        cache_seqlens: [batch] - actual sequence lengths
+        block_table: [batch, max_num_blocks_per_seq] - maps logical to physical blocks
+        page_block_size: size of each page block
+        block_size: size of attention blocks (block_N)
+    """
+    batch, heads, dim = query.shape
+    heads_kv = key_cache.shape[2]
+    dim_v = value_cache.shape[3]
+    num_head_groups = heads // heads_kv
+    scale = dim**0.5
+
+    # Reconstruct the full key and value tensors from paged cache
+    max_cache_seqlen = max(cache_seqlens).item()
+    key_full = torch.zeros((batch, heads_kv, max_cache_seqlen, dim), dtype=key_cache.dtype, device=key_cache.device)
+    value_full = torch.zeros((batch, heads_kv, max_cache_seqlen, dim_v), dtype=value_cache.dtype, device=value_cache.device)
+
+    # Reconstruct full tensors from paged cache using block_table
+    for b in range(batch):
+        seq_len = cache_seqlens[b].item()
+        num_blocks_needed = int(math.ceil(seq_len / page_block_size))
+
+        for block_idx in range(num_blocks_needed):
+            physical_block_idx = block_table[b, block_idx].item()
+
+            # Calculate the range of tokens for this block
+            start_token = block_idx * page_block_size
+            end_token = min(start_token + page_block_size, seq_len)
+            actual_block_size = end_token - start_token
+
+            # Copy from paged cache to full tensors
+            key_full[b, :, start_token:end_token, :] = key_cache[physical_block_idx, :actual_block_size, :, :].transpose(0, 1)
+            value_full[b, :, start_token:end_token, :] = value_cache[physical_block_idx, :actual_block_size, :, :].transpose(0, 1)
+
+    # Reshape query for grouped attention
+    query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups)  # [batch_size, num_head_groups, heads_kv, dim]
+
+    # Compute attention scores
+    scores = einsum(query, key_full, "b g h d, b h s d -> b g h s")  # [batch_size, num_head_groups, heads_kv, seqlen_kv]
+
+    # Create sparse mask based on block_indices
+    sparse_mask = torch.zeros_like(scores)
+
+    # Apply sparse mask based on selected blocks
+    for b in range(batch):
+        for h in range(heads_kv):
+            valid_indices = block_indices[b, h]  # Extract indices for this batch and head
+            for idx in valid_indices:
+                if idx >= 0:  # Valid block index
+                    start_pos = idx * block_size
+                    end_pos = min(start_pos + block_size, max_cache_seqlen)
+                    sparse_mask[b, :, h, start_pos:end_pos] = 1
+
+    # Apply sparse mask
+    scores = scores.masked_fill(sparse_mask == 0, float("-inf"))
+
+    # Apply causal mask based on actual sequence lengths
+    range_len = torch.arange(scores.shape[-1], device=scores.device).unsqueeze(0)
+    cache_seqlens_expanded = cache_seqlens.unsqueeze(1)
+    pad_mask = range_len >= cache_seqlens_expanded
+    pad_mask = pad_mask[:, None, None, :]
+    scores = scores.masked_fill(pad_mask, float("-inf"))
+
+    # Compute attention weights
+    attention = F.softmax(scores / scale, dim=-1)
+
+    # Apply attention to values
+    out = einsum(attention, value_full, "b g h s, b h s d -> b g h d")  # [batch_size, num_head_groups, heads_kv, dim]
+
+    # Reshape output back to original format
+    out = rearrange(out, "b g h d -> b (h g) d")  # [batch_size, heads, dim]
+
+    return out
+
+
+def ref_program_fa(query, kcache, vcache, cache_seqlens, block_table):
+    # latency reference
+    # from flash_attn_interface import flash_attn_with_kvcache # fa3
+    from flash_attn import flash_attn_with_kvcache  # fa2
+
+    query = query.unsqueeze(1)
+    output = flash_attn_with_kvcache(query, kcache, vcache, cache_seqlens=cache_seqlens, block_table=block_table)
+    output = output.squeeze(1)
+    return output
+
+
+def main(args):
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = (
+        args.batch,
+        args.heads,
+        args.heads_kv,
+        args.max_cache_seqlen,
+        args.dim,
+        args.dim_v,
+    )
+    sparse_ratio = args.sparse_ratio
+    block_N = args.block_N
+    page_block_size = args.page_block_size
+    num_blocks = args.num_pages  # Use num_pages from args
+
+    # For dense case verification, set sparse_ratio to 0 to select all blocks
+    max_selected_blocks = int(math.ceil(max_cache_seqlen / block_N))
+    print("max_selected_blocks: ", max_selected_blocks)
+    dtype = torch.float16
+
+    # Generate random inputs
+    Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda")
+    cache_seqlens = torch.randint(max_cache_seqlen // 2, max_cache_seqlen + 1, (batch,), dtype=torch.int32, device="cuda")
+    print("cache_seqlens: ", cache_seqlens)
+
+    K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda")
+    V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda")
+
+    # Create paged KV cache
+    K_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim), dtype=dtype, device="cuda")
+    V_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim_v), dtype=dtype, device="cuda")
+
+    # Create block table and block indices for dense case (all blocks selected)
+    max_num_blocks_per_seq = int(math.ceil(max_cache_seqlen / page_block_size))
+    print("max_num_blocks_per_seq: ", max_num_blocks_per_seq)
+    block_table = torch.zeros((batch, max_num_blocks_per_seq), dtype=torch.int32, device="cuda")
+    block_indices = torch.zeros((batch, heads_kv, max_selected_blocks), dtype=torch.int32, device="cuda")
+
+    # Fill block table and block indices and cache
+
+    # Create a pool of available physical blocks
+    total_blocks_needed = sum(int(math.ceil(cache_seqlens[seq_idx].item() / page_block_size)) for seq_idx in range(batch))
+    available_blocks = list(range(total_blocks_needed))
+    import random
+
+    random.seed(42)  # For reproducibility
+    random.shuffle(available_blocks)
+
+    # Fill block table with random physical block indices
+    block_assignment = {}  # Map (seq_idx, block_idx) -> physical_block_idx
+    block_idx_counter = 0
+
+    for seq_idx in range(batch):
+        seq_len = cache_seqlens[seq_idx].item()
+        num_blocks_needed = int(math.ceil(seq_len / page_block_size))
+
+        # Assign random physical blocks for each sequence
+        for block_idx in range(num_blocks_needed):
+            physical_block_idx = available_blocks[block_idx_counter]
+            block_table[seq_idx, block_idx] = physical_block_idx
+            block_assignment[(seq_idx, block_idx)] = physical_block_idx
+            block_idx_counter += 1
+
+    print(f"Block table: {block_table}")
+
+    # Fill K_cache and V_cache with data from original K and V tensors using random block assignment
+    for seq_idx in range(batch):
+        seq_len = cache_seqlens[seq_idx].item()
+        num_blocks_needed = int(math.ceil(seq_len / page_block_size))
+
+        for block_idx in range(num_blocks_needed):
+            physical_block_idx = block_assignment[(seq_idx, block_idx)]
+
+            # Calculate the range of tokens for this block
+            start_token = block_idx * page_block_size
+            end_token = min(start_token + page_block_size, seq_len)
+            actual_block_size = end_token - start_token
+
+            # Copy K and V data to the paged cache
+            K_cache[physical_block_idx, :actual_block_size, :, :] = K[seq_idx, start_token:end_token, :, :]
+            V_cache[physical_block_idx, :actual_block_size, :, :] = V[seq_idx, start_token:end_token, :, :]
+
+    # Fill block_indices for sparse attention
+    # For dense case (verification), we select all blocks in reverse order
+    # For sparse case, we select a subset of blocks based on sparse_ratio
+    for seq_idx in range(batch):
+        seq_len = cache_seqlens[seq_idx].item()
+        num_tile = int(math.ceil(seq_len / block_N))
+
+        if sparse_ratio == 0.0:
+            # Dense case: select all blocks in reverse order
+            selected_blocks = min(num_tile, max_selected_blocks)
+            for head_idx in range(heads_kv):
+                for i in range(selected_blocks):
+                    # Select blocks in reverse order (most recent first)
+                    block_indices[seq_idx, head_idx, i] = num_tile - 1 - i
+                # Fill remaining slots with -1 (invalid)
+                for i in range(selected_blocks, max_selected_blocks):
+                    block_indices[seq_idx, head_idx, i] = -1
+        else:
+            # Fill block_indices for all KV heads
+            num_selected = int(num_tile * (1.0 - sparse_ratio))
+            num_selected = max(1, min(num_selected, max_selected_blocks))
+            all_blocks = list(range(num_tile))
+            for head_idx in range(heads_kv):
+                selected_blocks = []
+                # Always include the most recent blocks
+                recent_blocks = 1
+                selected_blocks.append(num_tile - 1)
+
+                # Randomly select some earlier blocks
+                if num_selected > recent_blocks:
+                    remaining_blocks = [b for b in all_blocks if b not in selected_blocks]
+                    if remaining_blocks:
+                        import random
+
+                        random.seed(42)  # For reproducibility
+                        additional_blocks = random.sample(remaining_blocks, min(num_selected - recent_blocks, len(remaining_blocks)))
+                        selected_blocks.extend(additional_blocks)
+
+                # Sort selected blocks in reverse order (most recent first)
+                selected_blocks.sort(reverse=True)
+
+                for i in range(len(selected_blocks)):
+                    block_indices[seq_idx, head_idx, i] = selected_blocks[i]
+                # Fill remaining slots with -1 (invalid)
+                for i in range(len(selected_blocks), max_selected_blocks):
+                    block_indices[seq_idx, head_idx, i] = -1
+
+    # Initialize sparse attention module
+    sparse_attn = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, page_block_size, block_N, num_blocks)
+    output_sparse = sparse_attn.forward(Q, K_cache, V_cache, block_indices, cache_seqlens, block_table)
+
+    import flash_attn  # noqa: F401
+
+    output_ref_torch = ref_program_torch_paged(Q, K_cache, V_cache, block_indices, cache_seqlens, block_table, page_block_size, block_N)
+
+    output_ref_fa = ref_program_fa(Q, K_cache, V_cache, cache_seqlens, block_table)
+    # Check correctness
+    if sparse_ratio == 0.0:
+        max_diff = torch.max(torch.abs(output_sparse - output_ref_fa)).item()
+        mean_diff = torch.mean(torch.abs(output_sparse - output_ref_fa)).item()
+        assert torch.allclose(output_ref_fa, output_ref_torch, atol=1e-2), "Reference outputs do not match!"
+    else:
+        max_diff = torch.max(torch.abs(output_sparse - output_ref_torch)).item()
+        mean_diff = torch.mean(torch.abs(output_sparse - output_ref_torch)).item()
+
+    print(f"Max difference: {max_diff:.6f}")
+    print(f"Mean difference: {mean_diff:.6f}")
+
+    if max_diff < 1e-2:
+        print("✓ Verification PASSED: Results match within tolerance")
+    else:
+        print("✗ Verification FAILED: Results differ significantly")
+
+    # Performance measurement
+    for _ in range(10):  # Warm-up
+        sparse_attn.forward(Q, K_cache, V_cache, block_indices, cache_seqlens, block_table)
+
+    torch.cuda.synchronize()
+    start_time = time.time()
+    for _ in range(100):  # Run multiple times for averaging
+        sparse_attn.forward(Q, K_cache, V_cache, block_indices, cache_seqlens, block_table)
+    torch.cuda.synchronize()
+    end_time = time.time()
+
+    kernel_time = (end_time - start_time) / 100 * 1000  # Convert to ms
+    print(f"Kernel execution time: {kernel_time:.2f} ms")
+
+    # FA performance measurement
+    for _ in range(10):  # Warm-up
+        ref_program_fa(Q, K_cache, V_cache, cache_seqlens, block_table)
+
+    torch.cuda.synchronize()
+    start_time_fa = time.time()
+    for _ in range(100):  # Run multiple times for averaging
+        ref_program_fa(Q, K_cache, V_cache, cache_seqlens, block_table)
+    torch.cuda.synchronize()
+    end_time_fa = time.time()
+    kernel_time_fa = (end_time_fa - start_time_fa) / 100 * 1000  # Convert to ms
+    print(f"FA kernel execution time: {kernel_time_fa:.2f} ms")
+
+    print(f"Speedup: {kernel_time_fa / kernel_time:.2f}x")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch", type=int, default=8, help="batch size")
+    parser.add_argument("--heads", type=int, default=32, help="heads")
+    parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv")
+    parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length")
+    parser.add_argument("--dim", type=int, default=128, help="dim")
+    parser.add_argument("--dim_v", type=int, default=128, help="dim_v")
+    parser.add_argument("--sparse_ratio", type=float, default=0.0, help="sparse ratio")
+    parser.add_argument("--block_N", type=int, default=64, help="block_N")
+    parser.add_argument("--page_block_size", type=int, default=256, help="block size of pages")
+    parser.add_argument("--num_pages", type=int, default=1024, help="total number of pages")
+    args = parser.parse_args()
+    main(args)

tilelang/original/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py

+import torch
+import torch.nn.functional as F
+import tilelang
+import tilelang.language as T
+from einops import rearrange, einsum
+import argparse
+import time
+import math
+from heuristic import num_splits_heuristic
+
+
+def flashattn(batch, heads, heads_kv, dim, dim_v):
+    scale = (1.0 / dim) ** 0.5 * 1.44269504  # log2(e)
+    dtype = T.float16
+    accum_dtype = T.float32
+    kv_group_num = heads // heads_kv
+
+    @tilelang.jit(
+        out_idx=[-1],
+        pass_configs={
+            tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+        },
+    )
+    def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seqlen, max_selected_blocks):
+        shape_q = [batch, heads, dim]
+        shape_k = [batch, max_cache_seqlen, heads_kv, dim]
+        shape_v = [batch, max_cache_seqlen, heads_kv, dim_v]
+        shape_indices = [batch, heads_kv, max_selected_blocks]
+        shape_o = [batch, heads, dim_v]
+        part_shape = [batch, heads, num_split, dim_v]
+        valid_block_H = min(block_H, kv_group_num)
+
+        @T.macro
+        def flash_attn_split(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            block_indices: T.Tensor(shape_indices, T.int32),
+            cache_seqlens: T.Tensor([batch], T.int32),
+            # actual_num_blocks: T.Tensor([batch], T.int32),
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+        ):
+            with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz):
+                Q_shared = T.alloc_shared([block_H, dim], dtype)
+                K_shared = T.alloc_shared([block_N, dim], dtype)
+                V_shared = T.alloc_shared([block_N, dim_v], dtype)
+                # O_shared = T.alloc_shared([valid_block_H, dim_v], dtype)
+                acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+                acc_s_cast = T.alloc_fragment([block_H, block_N], dtype)
+                acc_o = T.alloc_fragment([block_H, dim_v], accum_dtype)
+
+                scores_max = T.alloc_fragment([block_H], accum_dtype)
+                scores_max_prev = T.alloc_fragment([block_H], accum_dtype)
+                scores_scale = T.alloc_fragment([block_H], accum_dtype)
+                scores_sum = T.alloc_fragment([block_H], accum_dtype)
+                logsum = T.alloc_fragment([block_H], accum_dtype)
+                has_valid_block = T.alloc_var("bool")
+
+                bid = bx
+                hid = by
+                sid = bz
+                cur_kv_head = hid // (kv_group_num // valid_block_H)
+
+                T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared)
+                T.fill(acc_o, 0)
+                T.fill(logsum, 0)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+
+                num_blocks = max_selected_blocks
+                blocks_per_split = T.floordiv(num_blocks, num_split)
+                remaining_blocks = T.floormod(num_blocks, num_split)
+                loop_range = blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0)
+                start = blocks_per_split * sid + T.min(sid, remaining_blocks)
+                has_valid_block = False
+
+                for k in T.Pipelined(loop_range, num_stages=num_stages):
+                    i_s = block_indices[bid, cur_kv_head, start + k]
+                    if i_s >= 0:
+                        has_valid_block = True
+                        T.copy(K[bid, i_s * block_N : (i_s + 1) * block_N, cur_kv_head, :], K_shared)
+                        T.clear(acc_s)
+                        T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+                        if k == 0:  # assume block_indices is sorted in reverse order, otherwise, remove this if condition
+                            for i, j in T.Parallel(block_H, block_N):
+                                acc_s[i, j] = T.if_then_else(i_s * block_N + j >= cache_seqlens[bid], -T.infinity(accum_dtype), acc_s[i, j])
+                        T.copy(scores_max, scores_max_prev)
+                        T.fill(scores_max, -T.infinity(accum_dtype))
+                        T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                        for i in T.Parallel(block_H):
+                            scores_max[i] = T.max(scores_max[i], scores_max_prev[i])
+                            scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+                        for i, j in T.Parallel(block_H, block_N):
+                            acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+                        T.reduce_sum(acc_s, scores_sum, dim=1)
+                        for i in T.Parallel(block_H):
+                            logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+                        T.copy(acc_s, acc_s_cast)
+                        for i, j in T.Parallel(block_H, dim_v):
+                            acc_o[i, j] *= scores_scale[i]
+                        T.copy(V[bid, i_s * block_N : (i_s + 1) * block_N, cur_kv_head, :], V_shared)
+                        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+                if has_valid_block:
+                    for i, j in T.Parallel(block_H, dim_v):
+                        acc_o[i, j] /= logsum[i]
+
+                    for i in T.Parallel(block_H):
+                        logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
+
+                for i in T.Parallel(block_H):
+                    if i < valid_block_H:
+                        glse[bid, hid * valid_block_H + i, sid] = logsum[i]
+
+                for i, j in T.Parallel(block_H, dim_v):
+                    if i < valid_block_H:
+                        Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j]
+
+        @T.macro
+        def combine(
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+            Output: T.Tensor(shape_o, dtype),
+        ):
+            with T.Kernel(heads, batch, threads=128) as (by, bz):
+                po_local = T.alloc_fragment([dim_v], accum_dtype)
+                o_accum_local = T.alloc_fragment([dim_v], accum_dtype)
+                lse_local_split = T.alloc_local([1], accum_dtype)
+                lse_logsum_local = T.alloc_local([1], accum_dtype)
+                lse_max_local = T.alloc_local([1], accum_dtype)
+                scale_local = T.alloc_local([1], accum_dtype)
+                max_split = T.alloc_local([1], T.int32)
+
+                T.annotate_layout(
+                    {
+                        lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i),
+                    }
+                )
+
+                T.clear(lse_logsum_local)
+                T.clear(o_accum_local)
+                lse_max_local[0] = -T.infinity(accum_dtype)
+                for k in T.serial(num_split):
+                    lse_local_split[0] = glse[bz, by, k]
+                    if lse_local_split[0] != 0:
+                        max_split[0] = k
+                        lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k])
+
+                for k in T.Pipelined(num_split, num_stages=1):
+                    if k <= max_split[0]:
+                        lse_local_split[0] = glse[bz, by, k]
+                        lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0])
+                lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0]
+                for k in T.serial(num_split):
+                    if k <= max_split[0]:
+                        for i in T.Parallel(dim_v):
+                            po_local[i] = Output_partial[bz, by, k, i]
+                        lse_local_split[0] = glse[bz, by, k]
+                        scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0])
+                        for i in T.Parallel(dim_v):
+                            o_accum_local[i] += po_local[i] * scale_local[0]
+                for i in T.Parallel(dim_v):
+                    Output[bz, by, i] = o_accum_local[i]
+
+        @T.prim_func
+        def main(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            block_indices: T.Tensor(shape_indices, T.int32),
+            cache_seqlens: T.Tensor([batch], T.int32),
+            # actual_num_blocks: T.Tensor([batch], T.int32),
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+            Output: T.Tensor(shape_o, dtype),
+        ):
+            # flash_attn_split(Q, K, V, block_indices, cache_seqlens, actual_num_blocks, glse, Output_partial)
+            flash_attn_split(Q, K, V, block_indices, cache_seqlens, glse, Output_partial)
+            combine(glse, Output_partial, Output)
+
+        return main
+
+    return kernel_func
+
+
+class SparseFlashAttn(torch.nn.Module):
+    def __init__(self, batch, heads, heads_kv, dim, dim_v, block_size):
+        super(SparseFlashAttn, self).__init__()
+        self.batch = batch
+        self.heads = heads
+        self.heads_kv = heads_kv
+        self.dim = dim
+        self.dim_v = dim_v
+        self.block_size = block_size
+
+        self.block_H = 64
+
+        self.kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
+            block_N=block_size,
+            block_H=self.block_H,
+            num_split=T.dynamic("num_split"),
+            num_stages=2,
+            threads=128,
+            max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+            max_selected_blocks=T.dynamic("max_selected_blocks"),
+        )
+
+        props = torch.cuda.get_device_properties(torch.device("cuda:0"))
+        self.num_sm = props.multi_processor_count
+
+    def forward(self, query, key, value, block_indices, cache_seqlens):
+        batch = self.batch
+        heads = self.heads
+        heads_kv = self.heads_kv
+        dim_v = self.dim_v
+        dim = self.dim
+        block_size = self.block_size
+        max_selected_blocks = block_indices.shape[-1]
+
+        # Compute static scheduling parameters
+        num_m_blocks = 1 * (heads // heads_kv + self.block_H - 1) // self.block_H
+        num_n_blocks = max_selected_blocks
+        size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2
+        total_mblocks = batch * heads_kv * num_m_blocks
+
+        num_sm = self.num_sm
+
+        num_split = num_splits_heuristic(
+            total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128
+        )
+
+        glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda")
+        output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda")
+
+        output = self.kernel(query, key, value, block_indices, cache_seqlens, glse, output_partial)
+        return output
+
+
+def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, block_size):
+    """
+    Args:
+        query: [batch, heads, dim]
+        key: [batch, max_cache_seqlen, heads_kv, dim]
+        value: [batch, max_cache_seqlen, heads_kv, dim_v]
+        block_indices: [batch, heads_kv, max_selected_blocks], indices of selected blocks, -1 for padding
+        cache_seqlens: [batch], sequence lengths of the kvcache
+        max_cache_seqlen: maximum sequence length of kvcache
+        block_size: block size
+    Returns:
+        output: [batch, heads, dim_v]
+
+    """
+
+    batch, heads, dim = query.shape
+    heads_kv = key.shape[2]
+    dim_v = value.shape[-1]
+    max_selected_blocks = block_indices.shape[-1]
+    block_H = 64
+
+    actual_num_blocks = torch.sum(block_indices != -1, dim=-1).to(torch.int32)
+    actual_num_blocks = actual_num_blocks[
+        :, 0
+    ]  # [batch],  number of valid blocks, assume all groups in the same batch have the same number of blocks
+
+    # get num_split
+    num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H
+    num_n_blocks = max_selected_blocks  # (kv_seqlen  + block_size - 1 ) // block_size
+    # num_n_blocks = torch.sum(actual_num_blocks, dim=-1).item() * heads_kv # total number of blocks
+
+    size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2  # kv_seqlen * (dim + dim_v) * 2
+    total_mblocks = batch * heads_kv * num_m_blocks
+    num_sm = 132
+    num_split = num_splits_heuristic(
+        total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128
+    )
+
+    glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda")
+    Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda")
+    kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
+        block_N=block_size,
+        block_H=block_H,
+        num_split=T.dynamic("num_split"),
+        num_stages=2,
+        threads=128,
+        max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+        max_selected_blocks=T.dynamic("max_selected_blocks"),
+    )
+
+    output = kernel(query, key, value, block_indices, cache_seqlens, glse, Output_partial)
+    return output
+
+
+def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size):
+    batch, heads, dim = query.shape
+    heads_kv = key.shape[2]
+    num_head_groups = query.shape[1] // key.shape[2]
+    scale = dim**0.5
+    key = rearrange(key, "b n h d -> b h n d")  # [batch_size, heads_kv, seqlen_kv, dim]
+    value = rearrange(value, "b n h d -> b h n d")  # [batch_size, heads_kv, seqlen_kv, dim]
+
+    query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups)  # [batch_size, num_head_groups, heads_kv, dim]
+
+    scores = einsum(query, key, "b g h d, b h s d -> b g h s")  # [batch_size, num_head_groups, heads_kv, seqlen_kv]
+
+    sparse_mask = torch.zeros_like(scores)
+    # Assign mask values based on block_indices
+    for b in range(batch):
+        for h in range(heads_kv):
+            valid_indices = block_indices[b, h]  # Extract indices for this batch and head
+            for idx in valid_indices:
+                if idx >= 0:
+                    sparse_mask[b, :, h, idx * block_size : (idx + 1) * block_size] = 1
+    scores = scores.masked_fill(sparse_mask == 0, float("-inf"))
+
+    range_len = torch.arange(scores.shape[-1], device="cuda").unsqueeze(0)
+    cache_seqlens_expanded = cache_seqlens.unsqueeze(1)
+    pad_mask = range_len >= cache_seqlens_expanded
+    pad_mask = pad_mask[:, None, None, :]
+    scores = scores.masked_fill(pad_mask, float("-inf"))
+    attention = F.softmax(scores / scale, dim=-1)  # [batch_size, num_head_groups, heads_kv, seqlen_kv]
+
+    out = einsum(attention, value, "b g h s, b h s d -> b g h d")  # [batch_size, num_head_groups, heads_kv, dim]
+    out = rearrange(out, "b g h d -> b (h g) d")  # [batch_size, heads, dim]
+    return out
+
+
+def ref_program_fa(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size):
+    # latency reference
+    # from flash_attn_interface import flash_attn_with_kvcache # fa3
+    from flash_attn import flash_attn_with_kvcache  # fa2
+
+    query = query.unsqueeze(1)
+    output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens)
+    output = output.squeeze(1)
+    return output
+
+
+def debug(name, expect, actual, atol=1e-3, rtol=1e-3):
+    all_close = torch.allclose(expect, actual, atol=atol, rtol=rtol)
+    print(name + "  all_close={}".format(all_close))
+    if not all_close:
+        diff = (expect - actual).abs()
+        print("all_close={}, max={}, min={}, mean={}".format(all_close, diff.max().item(), diff.min().item(), diff.mean().item()))
+        max_indices = torch.nonzero(diff == diff.max().item())
+        first_index = tuple(max_indices[0].tolist())
+        print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}")
+
+
+def main(batch=8, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32):
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v
+    sparse_ratio = sparse_ratio
+    block_size = block_size
+    max_selected_blocks = int(math.ceil(max_cache_seqlen * (1 - sparse_ratio) / block_size))
+    print("max_selected_blocks: ", max_selected_blocks)
+    dtype = torch.float16
+
+    Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda")
+    K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda")
+    V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda")
+    cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda")
+    # cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda')
+    # # Ensure at least one element equals cache_seqlen
+    # random_index = torch.randint(0, batch, (1,), device='cuda').item()  # Select a random index
+    # # cache_seqlens[random_index] = max_cache_seqlen  # Assign cache_seqlen to ensure at least one occurrence
+
+    print("cache_seqlens: ", cache_seqlens)
+
+    max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int()
+    print("max_valid_num_blocks: ", max_valid_num_blocks)
+    # Initialize block_indices with -1 (for padding blocks)
+    block_indices = torch.full((batch, heads_kv, max_selected_blocks), -1, dtype=torch.int32, device="cuda")
+    # max_num_blocks = int((max_cache_seqlen + block_size - 1)/ block_size)
+    # block_indices = torch.full((batch, heads_kv, max_num_blocks), -1, dtype=torch.int32, device='cuda')
+
+    # Assign valid indices while ensuring no duplicates within each batch-group
+    for b in range(batch):
+        max_valid_block = max_valid_num_blocks[b].item()  # Max valid blocks for this batch
+        if max_valid_block > 0:  # Ensure there's at least one valid block
+            for h in range(heads_kv):
+                valid_indices = torch.randperm(max_valid_block, device="cuda", dtype=torch.int32)[:max_selected_blocks]
+                # valid_indices = torch.randperm(max_valid_block, device='cuda', dtype=torch.int32)[:max_num_blocks]
+                block_indices[b, h, : len(valid_indices)] = valid_indices
+
+    # Sort indices within each batch-group for consistency
+    block_indices, _ = block_indices.sort(dim=-1, descending=True)
+    # print("block_indices: ", block_indices)
+    actual_num_blocks = torch.sum(block_indices != -1, dim=-1).to(torch.int32)[:, 0]
+    print("actual_num_blocks: ", actual_num_blocks)
+    # print(block_indices.shape, actual_num_blocks.shape)
+
+    max_num_blocks = torch.max(max_valid_num_blocks).item()
+    print("max_num_blocks: ", max_num_blocks)
+
+    # parity reference
+    ref = ref_program_torch(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
+
+    sparse_kernel = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, block_size)
+    out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
+    debug("output", ref, out, atol=1e-3, rtol=1e-3)
+
+    import flash_attn  # noqa: F401
+
+    ## latency reference
+    for _ in range(10):
+        ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
+    torch.cuda.synchronize()
+    start = time.time()
+    for _ in range(100):
+        ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
+    torch.cuda.synchronize()
+    print("dense time: ", (time.time() - start) / 100 * 1000)
+
+    for _ in range(10):
+        # out = sparse_gqa_decode_varlen_indice(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, block_size)
+        out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
+    torch.cuda.synchronize()
+    start = time.time()
+    for _ in range(100):
+        # out = sparse_gqa_decode_varlen_indice(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, block_size)
+        out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
+    torch.cuda.synchronize()
+    print("sparse time: ", (time.time() - start) / 100 * 1000)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch", type=int, default=8, help="batch size")
+    parser.add_argument("--heads", type=int, default=32, help="heads")
+    parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv")
+    parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length")
+    parser.add_argument("--dim", type=int, default=128, help="dim")
+    parser.add_argument("--dim_v", type=int, default=128, help="dim_v")
+    parser.add_argument("--sparse_ratio", type=float, default=0.8, help="sparse ratio")
+    parser.add_argument("--block_size", type=int, default=32, help="block_size")
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, args.sparse_ratio, args.block_size)

tilelang/original/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py

+import torch
+import torch.nn.functional as F
+import tilelang
+from tilelang.autotuner import *
+import tilelang.language as T
+from einops import rearrange, einsum
+import argparse
+
+import time
+import math
+from heuristic import num_splits_heuristic
+
+
+def flashattn(batch, heads, heads_kv, dim, dim_v):
+    scale = (1.0 / dim) ** 0.5 * 1.44269504  # log2(e)
+    dtype = T.float16
+    accum_dtype = T.float32
+    kv_group_num = heads // heads_kv
+
+    @tilelang.jit(
+        out_idx=[-1],
+        pass_configs={
+            tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+        },
+    )
+    def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seqlen, num_blocks):
+        shape_q = [batch, heads, dim]
+        shape_k = [batch, max_cache_seqlen, heads_kv, dim]
+        shape_v = [batch, max_cache_seqlen, heads_kv, dim_v]
+        shape_mask = [batch, heads_kv, num_blocks]
+        shape_o = [batch, heads, dim_v]
+        part_shape = [batch, heads, num_split, dim_v]
+        valid_block_H = min(block_H, kv_group_num)
+
+        @T.macro
+        def flash_attn_split(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            block_mask: T.Tensor(shape_mask, T.bool),
+            cache_seqlens: T.Tensor([batch], T.int32),
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+        ):
+            with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz):
+                Q_shared = T.alloc_shared([block_H, dim], dtype)
+                K_shared = T.alloc_shared([block_N, dim], dtype)
+                V_shared = T.alloc_shared([block_N, dim_v], dtype)
+                # O_shared = T.alloc_shared([valid_block_H, dim_v], dtype)
+                acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+                acc_s_cast = T.alloc_fragment([block_H, block_N], dtype)
+                acc_o = T.alloc_fragment([block_H, dim_v], accum_dtype)
+
+                scores_max = T.alloc_fragment([block_H], accum_dtype)
+                scores_max_prev = T.alloc_fragment([block_H], accum_dtype)
+                scores_scale = T.alloc_fragment([block_H], accum_dtype)
+                scores_sum = T.alloc_fragment([block_H], accum_dtype)
+                logsum = T.alloc_fragment([block_H], accum_dtype)
+                has_valid_block = T.alloc_var("bool")
+
+                bid = bx
+                hid = by
+                sid = bz
+                cur_kv_head = hid // (kv_group_num // valid_block_H)
+
+                T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared)
+                T.fill(acc_o, 0)
+                T.fill(logsum, 0)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+                blocks_per_split = T.floordiv(num_blocks, num_split)
+                remaining_blocks = T.floormod(num_blocks, num_split)
+                loop_range = blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0)
+                start = blocks_per_split * sid + T.min(sid, remaining_blocks)
+                has_valid_block = False
+                for k in T.Pipelined(loop_range, num_stages=num_stages):
+                    if block_mask[bid, hid, start + k]:
+                        has_valid_block = True
+                        T.copy(K[bid, (start + k) * block_N : (start + k + 1) * block_N, cur_kv_head, :], K_shared)
+                        T.clear(acc_s)
+                        T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+                        for i, j in T.Parallel(block_H, block_N):
+                            acc_s[i, j] = T.if_then_else(
+                                (start + k) * block_N + j >= cache_seqlens[bx], -T.infinity(accum_dtype), acc_s[i, j]
+                            )
+                        T.copy(scores_max, scores_max_prev)
+                        T.fill(scores_max, -T.infinity(accum_dtype))
+                        T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                        for i in T.Parallel(block_H):
+                            scores_max[i] = T.max(scores_max[i], scores_max_prev[i])
+                            scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+                        for i, j in T.Parallel(block_H, block_N):
+                            acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+                        T.reduce_sum(acc_s, scores_sum, dim=1)
+                        for i in T.Parallel(block_H):
+                            logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+                        T.copy(acc_s, acc_s_cast)
+                        for i, j in T.Parallel(block_H, dim_v):
+                            acc_o[i, j] *= scores_scale[i]
+                        T.copy(V[bid, (start + k) * block_N : (start + k + 1) * block_N, cur_kv_head, :], V_shared)
+                        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+                if has_valid_block:
+                    for i, j in T.Parallel(block_H, dim_v):
+                        acc_o[i, j] /= logsum[i]
+                    for i in T.Parallel(block_H):
+                        logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
+
+                for i in T.Parallel(block_H):
+                    if i < valid_block_H:
+                        glse[bid, hid * valid_block_H + i, sid] = logsum[i]
+
+                for i, j in T.Parallel(block_H, dim_v):
+                    if i < valid_block_H:
+                        Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j]
+
+        @T.macro
+        def combine(
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+            Output: T.Tensor(shape_o, dtype),
+        ):
+            with T.Kernel(heads, batch, threads=128) as (by, bz):
+                po_local = T.alloc_fragment([dim_v], accum_dtype)
+                o_accum_local = T.alloc_fragment([dim_v], accum_dtype)
+                lse_local_split = T.alloc_local([1], accum_dtype)
+                lse_logsum_local = T.alloc_local([1], accum_dtype)
+                lse_max_local = T.alloc_local([1], accum_dtype)
+                scale_local = T.alloc_local([1], accum_dtype)
+
+                T.annotate_layout(
+                    {
+                        lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i),
+                    }
+                )
+
+                T.clear(lse_logsum_local)
+                T.clear(o_accum_local)
+                lse_max_local[0] = -T.infinity(accum_dtype)
+                for k in T.serial(num_split):
+                    lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k])
+                for k in T.Pipelined(num_split, num_stages=1):
+                    lse_local_split[0] = glse[bz, by, k]
+                    lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0])
+                lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0]
+                for k in T.serial(num_split):
+                    for i in T.Parallel(dim_v):
+                        po_local[i] = Output_partial[bz, by, k, i]
+                    lse_local_split[0] = glse[bz, by, k]
+                    scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0])
+                    for i in T.Parallel(dim_v):
+                        o_accum_local[i] += po_local[i] * scale_local[0]
+                for i in T.Parallel(dim_v):
+                    Output[bz, by, i] = o_accum_local[i]
+
+        @T.prim_func
+        def main(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            block_mask: T.Tensor(shape_mask, T.bool),
+            cache_seqlens: T.Tensor([batch], T.int32),
+            glse: T.Tensor([batch, heads, num_split], accum_dtype),
+            Output_partial: T.Tensor(part_shape, accum_dtype),
+            Output: T.Tensor(shape_o, dtype),
+        ):
+            flash_attn_split(Q, K, V, block_mask, cache_seqlens, glse, Output_partial)
+            combine(glse, Output_partial, Output)
+
+        return main
+
+    return kernel_func
+
+
+class SparseFlashAttn(torch.nn.Module):
+    def __init__(self, batch, heads, heads_kv, dim, dim_v, block_size):
+        super(SparseFlashAttn, self).__init__()
+        self.batch = batch
+        self.heads = heads
+        self.heads_kv = heads_kv
+        self.dim = dim
+        self.dim_v = dim_v
+        self.block_size = block_size
+
+        self.block_H = 64
+
+        self.kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
+            block_N=block_size,
+            block_H=self.block_H,
+            num_split=T.dynamic("num_split"),
+            num_stages=2,
+            threads=128,
+            max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+            num_blocks=T.dynamic("num_blocks"),
+        )
+
+        props = torch.cuda.get_device_properties(torch.device("cuda:0"))
+        self.num_sm = props.multi_processor_count
+
+    def forward(self, query, key, value, block_mask, cache_seqlens):
+        batch = self.batch
+        heads = self.heads
+        heads_kv = self.heads_kv
+        dim_v = self.dim_v
+        dim = self.dim
+        block_size = self.block_size
+        block_H = self.block_H
+        max_cache_seqlen = key.shape[1]
+        # get num_split
+        max_selected_blocks = (max_cache_seqlen + block_size - 1) // block_size
+        num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H
+        num_n_blocks = max_selected_blocks
+
+        size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2  # kv_seqlen * (dim + dim_v) * 2
+        total_mblocks = batch * heads_kv * num_m_blocks
+        # num_sm = 132
+        num_sm = self.num_sm
+        num_split = num_splits_heuristic(
+            total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128
+        )
+        # print("num_split: ", num_split)
+        glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda")
+        Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda")
+        output = self.kernel(query, key, value, block_mask, cache_seqlens, glse, Output_partial)
+        return output
+
+
+def sparse_gqa_decode_varlen_mask(query, key, value, block_mask, cache_seqlens, block_size):
+    """
+    Args:
+        query: [batch, heads, dim]
+        key: [batch, max_cache_seqlen, heads_kv, dim]
+        value: [batch, max_cache_seqlen, heads_kv, dim_v]
+        block_mask: [batch, heads_kv, num_blocks], mask for valid blocks
+        cache_seqlens: [batch], sequence lengths of the kvcache
+        block_size: block size
+    Returns:
+        output: [batch, heads, dim_v]
+
+    """
+
+    batch, heads, dim = query.shape
+    heads_kv = key.shape[2]
+    dim_v = value.shape[-1]
+    block_H = 64
+
+    actual_num_blocks = torch.sum(block_mask, dim=-1).to(torch.int32)
+    actual_num_blocks = actual_num_blocks[
+        :, 0
+    ]  # [batch],  number of valid blocks, assume all groups in the same batch have the same number of blocks
+    max_selected_blocks = actual_num_blocks.max().item()
+    # get num_split
+    num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H
+    num_n_blocks = max_selected_blocks  # (kv_seqlen  + block_size - 1 ) // block_size
+    # num_n_blocks = torch.sum(actual_num_blocks, dim=-1).item() * heads_kv # total number of blocks
+
+    size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2  # kv_seqlen * (dim + dim_v) * 2
+    total_mblocks = batch * heads_kv * num_m_blocks
+    num_sm = 132
+    num_split = num_splits_heuristic(
+        total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128
+    )
+
+    kernel = flashattn(batch, heads, heads_kv, dim, dim_v)(
+        block_N=block_size,
+        block_H=block_H,
+        num_split=T.dynamic("num_split"),
+        num_stages=2,
+        threads=128,
+        max_cache_seqlen=T.dynamic("max_cache_seqlen"),
+        num_blocks=T.dynamic("num_blocks"),
+    )
+    glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda")
+    Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda")
+    # print(kernel.get_kernel_source())
+
+    output = kernel(query, key, value, block_mask, cache_seqlens, glse, Output_partial)
+
+    return output
+
+
+def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size):
+    batch, heads, dim = query.shape
+    heads_kv = key.shape[2]
+
+    num_head_groups = query.shape[1] // key.shape[2]
+    scale = dim**0.5
+    key = rearrange(key, "b n h d -> b h n d")  # [batch_size, heads_kv, seqlen_kv, dim]
+    value = rearrange(value, "b n h d -> b h n d")  # [batch_size, heads_kv, seqlen_kv, dim]
+
+    query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups)  # [batch_size, num_head_groups, heads_kv, dim]
+
+    scores = einsum(query, key, "b g h d, b h s d -> b g h s")  # [batch_size, num_head_groups, heads_kv, seqlen_kv]
+
+    sparse_mask = torch.zeros_like(scores)
+    # Assign mask values
+    for b in range(batch):
+        for h in range(heads_kv):
+            for idx in range(num_blocks):
+                if block_mask[b, h, idx]:
+                    sparse_mask[b, :, h, idx * block_size : (idx + 1) * block_size] = 1
+
+    scores = scores.masked_fill(sparse_mask == 0, float("-inf"))
+
+    range_len = torch.arange(scores.shape[-1], device="cuda").unsqueeze(0)
+    cache_seqlens_expanded = cache_seqlens.unsqueeze(1)
+    pad_mask = range_len >= cache_seqlens_expanded
+    pad_mask = pad_mask[:, None, None, :]
+    scores = scores.masked_fill(pad_mask, float("-inf"))
+    attention = F.softmax(scores / scale, dim=-1)  # [batch_size, num_head_groups, heads_kv, seqlen_kv]
+
+    out = einsum(attention, value, "b g h s, b h s d -> b g h d")  # [batch_size, num_head_groups, heads_kv, dim]
+    out = rearrange(out, "b g h d -> b (h g) d")  # [batch_size, heads, dim]
+    return out
+
+
+def ref_program_fa(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size):
+    # latency reference
+    # from flash_attn_interface import flash_attn_with_kvcache # fa3
+    from flash_attn import flash_attn_with_kvcache  # fa2
+
+    query = query.unsqueeze(1)
+    output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens)
+    output = output.squeeze(1)
+    return output
+
+
+def debug(name, expect, actual, atol=1e-3, rtol=1e-3):
+    all_close = torch.allclose(expect, actual, atol=atol, rtol=rtol)
+    print(name + "  all_close={}".format(all_close))
+    if not all_close:
+        # print(expect[3, 28])
+        # print(actual[3, 28])
+        diff = (expect - actual).abs()
+        print("all_close={}, max={}, min={}, mean={}".format(all_close, diff.max().item(), diff.min().item(), diff.mean().item()))
+        max_indices = torch.nonzero(diff == diff.max().item())
+        first_index = tuple(max_indices[0].tolist())
+        print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}")
+
+
+def main(batch=8, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32):
+    batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v
+    sparse_ratio = sparse_ratio
+    block_size = block_size
+    max_selected_blocks = int(math.ceil(max_cache_seqlen * (1 - sparse_ratio) / block_size))
+    print("max_selected_blocks: ", max_selected_blocks)
+    dtype = torch.float16
+
+    Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda")
+    K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda")
+    V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda")
+    cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda")
+    # Ensure at least one element equals cache_seqlen
+    random_index = torch.randint(0, batch, (1,), device="cuda").item()  # Select a random index
+    cache_seqlens[random_index] = max_cache_seqlen  # Assign cache_seqlen to ensure at least one occurrence
+    # cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda')
+
+    print("cache_seqlens: ", cache_seqlens)
+
+    num_blocks = (max_cache_seqlen + block_size - 1) // block_size
+
+    valid_num_blocks = torch.ceil(cache_seqlens * (1 - sparse_ratio) / block_size).int()
+    print("valid_num_blocks: ", valid_num_blocks)
+    max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int()
+    print("max_valid_num_blocks: ", max_valid_num_blocks)
+    # Initialize block_mask with false (for padding blocks)
+    block_mask = torch.zeros((batch, heads_kv, num_blocks), dtype=torch.bool, device="cuda")
+
+    # Assign valid indices while ensuring no duplicates within each batch-group
+    for b in range(batch):
+        max_valid_block = max_valid_num_blocks[b].item()  # Max valid blocks for this batch
+        valid_num_block = valid_num_blocks[b].item()  # Valid blocks for this batch
+        if valid_num_block > 0:  # Ensure there's at least one valid block
+            for h in range(heads_kv):
+                perm = torch.randperm(max_valid_block, device="cuda")[:valid_num_block]
+                block_mask[b, h, perm] = True
+    # print("block_mask: ", block_mask)
+
+    # parity reference
+    ref = ref_program_torch(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size)
+    # out = sparse_gqa_decode_varlen_mask(Q, K, V, block_mask, cache_seqlens, block_size)
+    model = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, block_size)
+    out = model(Q, K, V, block_mask, cache_seqlens)
+    debug("output", ref, out, atol=1e-3, rtol=1e-3)
+
+    import flash_attn  # noqa: F401
+
+    ## latency reference
+    for _ in range(10):
+        ref = ref_program_fa(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size)
+    torch.cuda.synchronize()
+    start = time.time()
+    for _ in range(100):
+        ref = ref_program_fa(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size)
+    torch.cuda.synchronize()
+    print("dense time: ", (time.time() - start) / 100 * 1000)
+
+    for _ in range(10):
+        # out = sparse_gqa_decode_varlen_mask(Q, K, V, block_mask, cache_seqlens, block_size)
+        out = model(Q, K, V, block_mask, cache_seqlens)
+
+    torch.cuda.synchronize()
+    start = time.time()
+    for _ in range(100):
+        # out = sparse_gqa_decode_varlen_mask(Q, K, V, block_mask, cache_seqlens, block_size)
+        out = model(Q, K, V, block_mask, cache_seqlens)
+    torch.cuda.synchronize()
+    print("sparse time: ", (time.time() - start) / 100 * 1000)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch", type=int, default=8, help="batch size")
+    parser.add_argument("--heads", type=int, default=32, help="heads")
+    parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv")
+    parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length")
+    parser.add_argument("--dim", type=int, default=128, help="dim")
+    parser.add_argument("--dim_v", type=int, default=128, help="dim_v")
+    parser.add_argument("--sparse_ratio", type=float, default=0.8, help="sparse ratio")
+    parser.add_argument("--block_size", type=int, default=32, help="block_size")
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, args.sparse_ratio, args.block_size)