From 7b7c6a657d8f4bb7a0e330dd91c03a3e81e802f4 Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Sat, 22 Feb 2025 13:05:08 +0000
Subject: [PATCH 01/14] =?UTF-8?q?Add=20fp8=20linear=20kernel;\n=20Add=20em?=
 =?UTF-8?q?pty=20cache=20to=20fit=20in=2016G=20VRAM;=20By=20'wkGCaSS=20-?=
 =?UTF-8?q?=20=E7=9F=A5=E4=B9=8E=20https://zhuanlan.zhihu.com/p/2549161122?=
 =?UTF-8?q?5'?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../ktransformers_ext/triton/fp8gemm.py       | 191 ++++++++++++++++++
 ktransformers/operators/linear.py             |  61 +++++-
 ktransformers/tests/triton_fp8gemm_test.py    |  73 +++++++
 ktransformers/util/custom_gguf.py             |   6 +
 ktransformers/util/utils.py                   |   2 +-
 5 files changed, 331 insertions(+), 2 deletions(-)
 create mode 100644 ktransformers/ktransformers_ext/triton/fp8gemm.py
 create mode 100644 ktransformers/tests/triton_fp8gemm_test.py

diff --git a/ktransformers/ktransformers_ext/triton/fp8gemm.py b/ktransformers/ktransformers_ext/triton/fp8gemm.py
new file mode 100644
index 0000000..4da4cfe
--- /dev/null
+++ b/ktransformers/ktransformers_ext/triton/fp8gemm.py
@@ -0,0 +1,191 @@
+from typing import Tuple
+
+import torch
+import triton
+import triton.language as tl
+from triton import Config
+
+
+@triton.jit
+def act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr):
+    """
+    Quantizes the input tensor `x_ptr` and stores the result in `y_ptr` and the scaling factor in `s_ptr`.
+
+    Args:
+        x_ptr (triton.Pointer): Pointer to the input tensor.
+        y_ptr (triton.Pointer): Pointer to the output tensor where quantized values will be stored.
+        s_ptr (triton.Pointer): Pointer to the output tensor where scaling factors will be stored.
+        BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance.
+
+    Returns:
+        None
+    """
+    pid = tl.program_id(axis=0)
+    offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    x = tl.load(x_ptr + offs).to(tl.float32)
+    s = tl.max(tl.abs(x)) / 448.
+    y = x / s
+    y = y.to(y_ptr.dtype.element_ty)
+    tl.store(y_ptr + offs, y)
+    tl.store(s_ptr + pid, s)
+
+
+def act_quant(x: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Quantizes the input tensor `x` using block-wise quantization.
+
+    Args:
+        x (torch.Tensor): The input tensor to be quantized. Must be contiguous and its last dimension size must be divisible by `block_size`.
+        block_size (int, optional): The size of the blocks to be used for quantization. Default is 128.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
+            - The quantized tensor with dtype `torch.float8_e4m3fn`.
+            - A tensor of scaling factors with dtype `torch.float32`.
+    """
+    assert x.is_contiguous(), 'Input tensor must be contiguous'
+    assert x.size(-1) % block_size == 0, f'Last dimension size must be divisible by block_size (block_size={block_size})'
+    y = torch.empty_like(x, dtype=torch.float8_e4m3fn)
+    s = x.new_empty(*x.size()[:-1], x.size(-1) // block_size, dtype=torch.float32)
+    grid = lambda meta: (triton.cdiv(x.numel(), meta['BLOCK_SIZE']), )
+    act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size)
+    return y, s
+
+
+@triton.jit
+def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
+    """
+    Dequantizes weights using the provided scaling factors and stores the result.
+
+    Args:
+        x_ptr (tl.pointer): Pointer to the quantized weights.
+        s_ptr (tl.pointer): Pointer to the scaling factors.
+        y_ptr (tl.pointer): Pointer to the output buffer for dequantized weights.
+        M (int): Number of rows in the weight matrix.
+        N (int): Number of columns in the weight matrix.
+        BLOCK_SIZE (tl.constexpr): Size of the block for tiling.
+
+    Returns:
+        None
+    """
+    pid_m = tl.program_id(axis=0)
+    pid_n = tl.program_id(axis=1)
+    n = tl.cdiv(N, BLOCK_SIZE)
+    offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    offs = offs_m[:, None] * N + offs_n[None, :]
+    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
+    x = tl.load(x_ptr + offs, mask=mask).to(tl.float32)
+    s = tl.load(s_ptr + pid_m * n + pid_n)
+    y = x * s
+    tl.store(y_ptr + offs, y, mask=mask)
+
+
+def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor:
+    """
+    Dequantizes the given weight tensor using the provided scale tensor.
+
+    Args:
+        x (torch.Tensor): The quantized weight tensor of shape (M, N).
+        s (torch.Tensor): The scale tensor of shape (M, N).
+        block_size (int, optional): The block size to use for dequantization. Defaults to 128.
+
+    Returns:
+        torch.Tensor: The dequantized weight tensor of the same shape as `x`.
+
+    Raises:
+        AssertionError: If `x` or `s` are not contiguous or if their dimensions are not 2.
+    """
+    assert x.is_contiguous() and s.is_contiguous(), 'Input tensors must be contiguous'
+    assert x.dim() == 2 and s.dim() == 2, 'Input tensors must have 2 dimensions'
+    M, N = x.size()
+    y = torch.empty_like(x, dtype=torch.get_default_dtype())
+    grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']), triton.cdiv(N, meta['BLOCK_SIZE']))
+    weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
+    return y
+
+
+fp8_gemm_configs = [
+    Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': 128}, num_stages=num_stages, num_warps=8)
+    for block_m in [16, 32, 64] for block_n in [32, 64, 128] for num_stages in [3, 4, 5, 6]
+]
+
+@triton.autotune(configs=fp8_gemm_configs, key=['N', 'K'])
+@triton.jit
+def fp8_gemm_kernel(a_ptr, b_ptr, c_ptr,
+                    a_s_ptr, b_s_ptr,
+                    M, N: tl.constexpr, K: tl.constexpr,
+                    BLOCK_SIZE_M: tl.constexpr,
+                    BLOCK_SIZE_N: tl.constexpr,
+                    BLOCK_SIZE_K: tl.constexpr):
+    """
+    Performs a matrix multiplication operation on FP8 matrices with scaling factors.
+
+    Args:
+        a_ptr (tl.tensor): Pointer to the first input matrix A.
+        b_ptr (tl.tensor): Pointer to the second input matrix B.
+        c_ptr (tl.tensor): Pointer to the output matrix C.
+        a_s_ptr (tl.tensor): Pointer to the scaling factors for matrix A.
+        b_s_ptr (tl.tensor): Pointer to the scaling factors for matrix B.
+        M (int): Number of rows in matrix A and C.
+        N (tl.constexpr): Number of columns in matrix B and C.
+        K (tl.constexpr): Number of columns in matrix A and rows in matrix B.
+        BLOCK_SIZE_M (tl.constexpr): Block size for the M dimension.
+        BLOCK_SIZE_N (tl.constexpr): Block size for the N dimension.
+        BLOCK_SIZE_K (tl.constexpr): Block size for the K dimension.
+
+    Returns:
+        None
+    """
+    pid_m = tl.program_id(axis=0)
+    pid_n = tl.program_id(axis=1)
+    k = tl.cdiv(K, BLOCK_SIZE_K)
+    offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
+    offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = a_ptr + offs_m[:, None] * K + offs_k[None, :]
+    b_ptrs = b_ptr + offs_n[None, :] * K + offs_k[:, None]
+    a_s_ptrs = a_s_ptr + offs_m * k
+    b_s_ptrs = b_s_ptr + (offs_n // BLOCK_SIZE_K) * k
+
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for i in range(k):
+        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - i * BLOCK_SIZE_K, other=0.0)
+        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - i * BLOCK_SIZE_K, other=0.0)
+        a_s = tl.load(a_s_ptrs)
+        b_s = tl.load(b_s_ptrs)
+        accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
+        a_ptrs += BLOCK_SIZE_K
+        b_ptrs += BLOCK_SIZE_K
+        a_s_ptrs += 1
+        b_s_ptrs += 1
+    c = accumulator.to(c_ptr.dtype.element_ty)
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :]
+    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
+    tl.store(c_ptrs, c, mask=mask)
+
+
+def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor):
+    """
+    Perform a matrix multiplication using FP8 precision.
+
+    Args:
+        a (torch.Tensor): The first input matrix, must be contiguous.
+        a_s (torch.Tensor): The scaling factor for the first input matrix, must be contiguous.
+        b (torch.Tensor): The second input matrix, must be contiguous.
+        b_s (torch.Tensor): The scaling factor for the second input matrix, must be contiguous.
+
+    Returns:
+        torch.Tensor: The result of the matrix multiplication.
+    """
+    assert a.is_contiguous() and b.is_contiguous(), 'Input tensors must be contiguous'
+    assert a_s.is_contiguous() and b_s.is_contiguous(), 'Scaling factor tensors must be contiguous'
+    K = a.size(-1)
+    M = a.numel() // K
+    N = b.size(0)
+    c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype())
+    grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']), triton.cdiv(N, META['BLOCK_SIZE_N']))
+    fp8_gemm_kernel[grid](a, b, c, a_s, b_s, M, N, K)
+    return c
\ No newline at end of file
diff --git a/ktransformers/operators/linear.py b/ktransformers/operators/linear.py
index 08a2cca..5aff964 100644
--- a/ktransformers/operators/linear.py
+++ b/ktransformers/operators/linear.py
@@ -25,6 +25,7 @@ from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marl
 )
 from ktransformers.operators.base_operator import BaseInjectedModule
 from transformers.configuration_utils import PretrainedConfig
+from ktransformers.ktransformers_ext.triton.fp8gemm import fp8_gemm, act_quant, weight_dequant
 from abc import ABC, abstractmethod
 import sys, os
 sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build"))
@@ -164,7 +165,65 @@ class KLinearTorch(KLinearBase):
         if self.has_bias:
             self.bias = None
 
-
+class KLinearFP8(KLinearBase):
+    marlin_q_w: torch.Tensor
+    marlin_s: torch.Tensor
+    g_idx: torch.Tensor
+    sort_indices: torch.Tensor
+    has_bias: bool
+    weight: torch.Tensor
+    scale_w: torch.Tensor
+    bias: torch.Tensor
+    def __init__(
+        self,
+        key: str,
+        gguf_loader: GGUFLoader,
+        config: PretrainedConfig,
+        orig_module: nn.Module = None,
+        device: str = "cuda",
+        block_size: int = 128,
+        **kwargs,
+    ):
+        super().__init__(key, gguf_loader, config, orig_module, device, **kwargs)
+        self.has_bias = False
+        self.dtype = torch.get_default_dtype()
+        self.block_size = block_size
+    
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.to(self.device)
+        orig_shape = list(x.shape)
+        orig_dtype = x.dtype
+        x = x.reshape(-1, orig_shape[-1])
+        x_quantized, scale_x = act_quant(x, self.block_size)
+        y = fp8_gemm(x_quantized, scale_x, self.weight, self.weight.scale)
+        if self.bias is not None:
+            y += self.bias
+        return y.to(orig_dtype).reshape(orig_shape)
+    
+    def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
+        if device is None: device = self.device
+        if w is None: 
+            w = self.load_weight(device=device) 
+        if isinstance(w, nn.Parameter):
+            self.weight = w.to(device)
+            self.has_bias = False
+        elif isinstance(w, tuple):
+            self.weight = w[0].to(device)
+            self.bias = w[1].to(device)
+            self.has_bias = True
+        else:
+            raise ValueError("Invalid weight type")
+        self.weight = self.weight.to(device)
+        if self.has_bias:
+            self.bias = self.bias.to(device)
+        
+    def unload(self):
+        if self.weight is not None:
+            self.weight = None
+        if self.has_bias:
+            self.bias = None
+        
+        
 class KLinearMarlin(KLinearBase):
     marlin_q_w: torch.Tensor
     marlin_s: torch.Tensor
diff --git a/ktransformers/tests/triton_fp8gemm_test.py b/ktransformers/tests/triton_fp8gemm_test.py
new file mode 100644
index 0000000..bb3801c
--- /dev/null
+++ b/ktransformers/tests/triton_fp8gemm_test.py
@@ -0,0 +1,73 @@
+import torch
+import torch.nn.functional as F
+from typing import Optional
+import pytest
+from typing import Tuple, Optional, Literal
+
+# use dir path
+import os
+import sys
+sys.path.insert(0, "/home/azure/ktransformers")
+print(sys.path)
+from ktransformers.ktransformers_ext.triton.fp8gemm import fp8_gemm, act_quant, weight_dequant
+from safetensors import safe_open
+
+world_size = 1
+rank = 0
+block_size = 128
+gemm_impl: Literal["bf16", "fp8"] = "bf16"
+# Assuming `fp8_gemm`, `act_quant`, `weight_dequant` and other relevant functions are already defined
+
+def test_fp8_gemm_vs_torch_matmul():
+    # Test case 1: Create random matrices of size (M, K) and (K, N)
+    M, K, N = 64, 128, 256  # Matrix dimensions
+    x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
+    weight = torch.randn(N, K, dtype=torch.bfloat16, device='cuda')
+
+    # Apply act_quant to both matrices
+    x_quantized, scale_x = act_quant(x, block_size)
+    weight_quantized, scale_w = act_quant(weight, block_size)
+    
+    # mk continous
+    x_quantized = x_quantized.contiguous()
+    weight_quantized = weight_quantized.contiguous()
+    scale_x = scale_x.contiguous()
+    scale_w = scale_w.contiguous()
+
+    # Perform fp8_gemm using the quantized tensors
+    result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight_quantized, scale_w)
+
+    # Perform torch.matmul using the original floating point tensors
+    result_torch_matmul = torch.matmul(x, weight.T)
+    print(f'result_torch_matmul: {result_torch_matmul.shape}')
+    print(f'result_fp8_gemm: {result_fp8_gemm.shape}')
+
+    print(f"result_fp8_gemm:\n {result_fp8_gemm}")
+    print(f"result_torch_matmul:\n {result_torch_matmul}")
+    
+def test_fp8_gemm_vs_torch_matmul_load():
+    file_path = "/mnt/data/model/DeepSeek-V3/model-00001-of-000163.safetensors"
+    with safe_open(file_path, framework="pt", device=0) as f:
+        weight = f.get_tensor("model.layers.0.mlp.down_proj.weight")
+        scale = f.get_tensor("model.layers.0.mlp.down_proj.weight_scale_inv")
+
+    # weight_dequant
+    weight_dequantized = weight_dequant(weight, scale)
+    print(f"weight_dequantized: {weight_dequantized.shape}")
+    N, K = weight_dequantized.shape
+    M = 64
+    x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
+    x_quantized, scale_x = act_quant(x, block_size)
+    
+    # Test case 1: quantized x matmal with undequantized weight
+    result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+    print(f"result_fp8_gemm:\n {result_fp8_gemm}")
+
+    # Perform torch.matmul using the original floating point tensors
+    result_torch_matmul = torch.matmul(x, weight_dequantized.to(torch.bfloat16).T)
+    print(f"result_torch_matmul:\n {result_torch_matmul}")
+
+if __name__ == "__main__":
+    test_fp8_gemm_vs_torch_matmul()
+    test_fp8_gemm_vs_torch_matmul_load()
+    
\ No newline at end of file
diff --git a/ktransformers/util/custom_gguf.py b/ktransformers/util/custom_gguf.py
index eaa1a7d..26afd39 100644
--- a/ktransformers/util/custom_gguf.py
+++ b/ktransformers/util/custom_gguf.py
@@ -127,6 +127,7 @@ GGML_BLOCK_SIZES = {
     "Q5_K": 2 + 2 + 12 + 256 // 8 + 256 // 2,
     "Q6_K": 256 // 2 + 256 // 4 + 256 // 16 + 2,
     "IQ4_XS": 2 + 2 + 256 // 2 + 256 // 64,
+    "FP8": 1,
 }
 
 GGML_ELEMENTS_PER_BLOCK = {
@@ -142,6 +143,7 @@ GGML_ELEMENTS_PER_BLOCK = {
     "Q5_K": 256,
     "Q6_K": 256,
     "IQ4_XS": 256,
+    "FP8": 1,
 }
 
 DATA_TYPES = {
@@ -158,6 +160,7 @@ DATA_TYPES = {
     "uint64": 10,
     "int64": 11,
     "float64": 12,
+    "FP8": 13,
 }
 
 class GGUFLoader:
@@ -393,6 +396,9 @@ def read_value(f, data_type):
         elem_type, count = struct.unpack("<IQ", f.read(4 + 8))
         return [read_value(f, elem_type) for _ in range(count)]
 
+    elif data_type == DATA_TYPES["FP8"]:
+        return struct.unpack("<B", f.read(1))[0]
+
     else:
         raise NotImplementedError(f"Data type {data_type} not implemented")
 
diff --git a/ktransformers/util/utils.py b/ktransformers/util/utils.py
index 7034ac9..81d007c 100644
--- a/ktransformers/util/utils.py
+++ b/ktransformers/util/utils.py
@@ -70,7 +70,7 @@ def load_cur_state_dict(module: nn.Module, gguf_loader: GGUFLoader, prefix: str
             target_dtype = torch.get_default_dtype()
             device = get_device(translated_key[:translated_key.rfind(".")], gguf_loader.tensor_device_map)
             print(f"loading {translated_key} to {device}")
-            # device = "cpu" if "embd" in translated_key else "cuda"
+            torch.cuda.empty_cache() # To fit in 16G VRAM. By "wkGCaSS - 知乎 https://zhuanlan.zhihu.com/p/25491611225"
             weights = gguf_loader.load_gguf_tensor(translated_key, device = device).to(dtype = target_dtype)
             set_param(module, name, weights)
             del weights

From 006e8c6abc6503921411db935efd80ad7f16032d Mon Sep 17 00:00:00 2001
From: Atream <boxin-zhang@qq.com>
Date: Sun, 23 Feb 2025 07:40:47 +0000
Subject: [PATCH 02/14] remove causal mask

---
 ktransformers/operators/models.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/ktransformers/operators/models.py b/ktransformers/operators/models.py
index 5d2e911..3877dbc 100644
--- a/ktransformers/operators/models.py
+++ b/ktransformers/operators/models.py
@@ -649,9 +649,12 @@ class KDeepseekV2Model(BaseInjectedModule):
         if per_layer_prefill_flag:
             causal_mask = None
         else:
-            causal_mask = self._update_causal_mask(
-                attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-            )
+            if os.name == 'nt':
+                causal_mask = self._update_causal_mask(
+                    attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+                )
+            else:
+                causal_mask = None
 
         # embed positions
         hidden_states = inputs_embeds

From 581a524f65db422011d9a8db99439d658223db6f Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Mon, 24 Feb 2025 11:16:23 +0000
Subject: [PATCH 03/14] Add data loader to read special weights for fp8; Add
 special weight process script

---
 .../ktransformers_ext/triton/fp8gemm.py       |   1 +
 ktransformers/operators/experts.py            |  11 +-
 ktransformers/operators/gate.py               |  13 +-
 ktransformers/operators/linear.py             |  38 ++--
 ...pSeek-V3-Chat-fp8-linear-ggml-experts.yaml |  63 ++++++
 ktransformers/tests/triton_fp8gemm_test.py    |  47 +++-
 ktransformers/util/custom_gguf.py             |  19 +-
 ktransformers/util/custom_loader.py           |  86 +++++++
 ktransformers/util/utils.py                   |  15 +-
 merge_tensors/merge_safetensor_gguf.py        | 214 ++++++++++++++++++
 10 files changed, 481 insertions(+), 26 deletions(-)
 create mode 100644 ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml
 create mode 100644 ktransformers/util/custom_loader.py
 create mode 100644 merge_tensors/merge_safetensor_gguf.py

diff --git a/ktransformers/ktransformers_ext/triton/fp8gemm.py b/ktransformers/ktransformers_ext/triton/fp8gemm.py
index 4da4cfe..7d5b72e 100644
--- a/ktransformers/ktransformers_ext/triton/fp8gemm.py
+++ b/ktransformers/ktransformers_ext/triton/fp8gemm.py
@@ -1,3 +1,4 @@
+# Adopted from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py
 from typing import Tuple
 
 import torch
diff --git a/ktransformers/operators/experts.py b/ktransformers/operators/experts.py
index 21b4830..1ea244a 100644
--- a/ktransformers/operators/experts.py
+++ b/ktransformers/operators/experts.py
@@ -245,7 +245,16 @@ class KExpertsCPU(KExpertsBase):
         down_type = None
 
         for key in keys:
-            if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
+            if self.gguf_loader.safetensor_loader is not None:
+                # using a temp ugly way to temprary load the tensor
+                gate = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_gate_exps.weight").numpy()
+                up = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_up_exps.weight").numpy()
+                down = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_down_exps.weight").numpy()
+                gate_type = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_gate_exps.ggml_type").item()
+                up_type = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_up_exps.ggml_type").item()
+                down_type = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_down_exps.ggml_type").item()
+            
+            elif key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info:
                 gate = self.gguf_loader.get_mmap_tensor(key + ".ffn_gate_exps.weight")
                 up = self.gguf_loader.get_mmap_tensor(key + ".ffn_up_exps.weight")
                 down = self.gguf_loader.get_mmap_tensor(key + ".ffn_down_exps.weight")
diff --git a/ktransformers/operators/gate.py b/ktransformers/operators/gate.py
index 52bb33a..d908093 100644
--- a/ktransformers/operators/gate.py
+++ b/ktransformers/operators/gate.py
@@ -67,7 +67,14 @@ class KMoEGateBase(ABC):
 
         for key in keys:
             key = ".".join(key.split(".")[:-1])
-            if key + ".ffn_gate_inp.weight" in self.gguf_loader.tensor_info:
+            if self.gguf_loader.safetensor_loader is not None:
+                targets = [".ffn_gate_inp.weight", ".exp_probs_b.bias"]
+                weight = self.gguf_loader.safetensor_loader.load_tensor(key + ".ffn_gate_inp.weight") 
+                e_score_correction_bias = self.gguf_loader.safetensor_loader.load_tensor(key + ".exp_probs_b.bias")
+                weight_type = weight.dtype
+                e_score_correction_bias_type = e_score_correction_bias.dtype
+                res = {"weight": weight, "e_score_correction_bias": e_score_correction_bias,  "weight_type": weight_type, "e_score_correction_bias_type": e_score_correction_bias_type}
+            elif key + ".ffn_gate_inp.weight" in self.gguf_loader.tensor_info:
                 targets = [".ffn_gate_inp.weight", ".exp_probs_b.bias"]
                 tensors = self.load_multi(key, targets, device=device)
                 weight = tensors[".ffn_gate_inp.weight"]
@@ -116,8 +123,8 @@ class KMoEGate(BaseInjectedModule, KMoEGateBase):
             self.orig_module.e_score_correction_bias = nn.Parameter(w["e_score_correction_bias"])
         else:
             raise ValueError("Invalid weight type")
-        self.orig_module.weight = self.orig_module.weight.to(device)
-        self.orig_module.e_score_correction_bias = self.orig_module.e_score_correction_bias.to(device)
+        self.orig_module.weight = nn.Parameter(self.orig_module.weight.to(device))
+        self.orig_module.e_score_correction_bias = nn.Parameter(self.orig_module.e_score_correction_bias.to(device))
 
     def unload(self):
         if self.weight is not None:
diff --git a/ktransformers/operators/linear.py b/ktransformers/operators/linear.py
index 5aff964..e778102 100644
--- a/ktransformers/operators/linear.py
+++ b/ktransformers/operators/linear.py
@@ -76,7 +76,13 @@ class KLinearBase(ABC):
             keys = [self.key]
 
         for key in keys:
-            if key + ".weight" in self.gguf_loader.tensor_file_map:
+            if self.gguf_loader.safetensor_loader is not None:
+                # using safetensor_loader
+                tensor = self.gguf_loader.safetensor_loader.load_tensor(key+'.weight')
+                weight_scale_inv = self.gguf_loader.safetensor_loader.load_tensor(key+'.weight_scale_inv')
+                return nn.Parameter(tensor), nn.Parameter(weight_scale_inv)
+                
+            elif key + ".weight" in self.gguf_loader.tensor_file_map:
                 if key + ".bias" in self.gguf_loader.tensor_file_map:
                     tensors = self.load_multi(key, ["weight", "bias"], device=device)
                     tensor = tensors["weight"]
@@ -166,6 +172,8 @@ class KLinearTorch(KLinearBase):
             self.bias = None
 
 class KLinearFP8(KLinearBase):
+    # this kernel requires special handling for weight
+    # Please load the weight file downloaded from KVCache.AI
     marlin_q_w: torch.Tensor
     marlin_s: torch.Tensor
     g_idx: torch.Tensor
@@ -191,26 +199,20 @@ class KLinearFP8(KLinearBase):
     
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = x.to(self.device)
-        orig_shape = list(x.shape)
-        orig_dtype = x.dtype
-        x = x.reshape(-1, orig_shape[-1])
+        orig_dtype = x.dtype        
         x_quantized, scale_x = act_quant(x, self.block_size)
-        y = fp8_gemm(x_quantized, scale_x, self.weight, self.weight.scale)
-        if self.bias is not None:
-            y += self.bias
-        return y.to(orig_dtype).reshape(orig_shape)
+        y = fp8_gemm(x_quantized, scale_x, self.weight, self.weight_scale_inv)
+        return y.to(dtype=orig_dtype)
     
     def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None):
         if device is None: device = self.device
         if w is None: 
             w = self.load_weight(device=device) 
-        if isinstance(w, nn.Parameter):
-            self.weight = w.to(device)
-            self.has_bias = False
-        elif isinstance(w, tuple):
+        ### TODO fit weight_inv format
+        if isinstance(w, tuple):
             self.weight = w[0].to(device)
-            self.bias = w[1].to(device)
-            self.has_bias = True
+            self.weight_scale_inv = w[1].to(device)
+            self.has_bias = False
         else:
             raise ValueError("Invalid weight type")
         self.weight = self.weight.to(device)
@@ -425,7 +427,8 @@ class KLinearCPUInfer(KLinearBase):
 LINEAR_MAP = {
     "KLinearMarlin": KLinearMarlin,
     "KLinearTorch": KLinearTorch,
-    "KLinearCPUInfer": KLinearCPUInfer
+    "KLinearCPUInfer": KLinearCPUInfer,
+    "KLinearFP8": KLinearFP8,
 }
 
 class KTransformersLinear(BaseInjectedModule, KLinearBase):
@@ -472,10 +475,11 @@ class KTransformersLinear(BaseInjectedModule, KLinearBase):
     def forward(self, x):
         if self.mode == InferenceState.PREFILL:
             assert self.prefill_linear is not None, "cpu linear is not initialized"
-            return self.prefill_linear.forward(x)
+            y = self.prefill_linear.forward(x)
         else:
             assert self.generate_linear is not None, "gpu linear is not initialized"
-            return self.generate_linear.forward(x)
+            y = self.generate_linear.forward(x)
+        return y
 
     def load(self, w: dict | nn.Parameter | tuple | None = None, mode: InferenceState = InferenceState.GENERATE):
         if not mode:
diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml
new file mode 100644
index 0000000..25f021e
--- /dev/null
+++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml
@@ -0,0 +1,63 @@
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    name: "^model\\.layers\\.(?!.*self_attn\\.kv_b_proj).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      generate_op: "KLinearFP8"
+      prefill_op: "KLinearTorch"
+- match:
+    name: "^model\\.layers\\..*\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\..*\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op: "KExpertsCPU"
+      out_device: "cuda"
+  recursive: False # don't recursively inject submodules of this module
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cpu"
+      prefill_device: "cpu"
\ No newline at end of file
diff --git a/ktransformers/tests/triton_fp8gemm_test.py b/ktransformers/tests/triton_fp8gemm_test.py
index bb3801c..58888d6 100644
--- a/ktransformers/tests/triton_fp8gemm_test.py
+++ b/ktransformers/tests/triton_fp8gemm_test.py
@@ -3,7 +3,7 @@ import torch.nn.functional as F
 from typing import Optional
 import pytest
 from typing import Tuple, Optional, Literal
-
+import time
 # use dir path
 import os
 import sys
@@ -56,18 +56,61 @@ def test_fp8_gemm_vs_torch_matmul_load():
     print(f"weight_dequantized: {weight_dequantized.shape}")
     N, K = weight_dequantized.shape
     M = 64
-    x = torch.randn(M, K, dtype=torch.bfloat16, device='cuda')
+    x = torch.randn(2 ,M, K, dtype=torch.bfloat16, device='cuda')
     x_quantized, scale_x = act_quant(x, block_size)
     
     # Test case 1: quantized x matmal with undequantized weight
     result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
     print(f"result_fp8_gemm:\n {result_fp8_gemm}")
+    print(f"dtype {result_fp8_gemm.dtype}")
 
     # Perform torch.matmul using the original floating point tensors
     result_torch_matmul = torch.matmul(x, weight_dequantized.to(torch.bfloat16).T)
     print(f"result_torch_matmul:\n {result_torch_matmul}")
 
+def test_fp8_gemm_tplops():
+    file_path = "/mnt/data/model/DeepSeek-V3/model-00001-of-000163.safetensors"
+    with safe_open(file_path, framework="pt", device=0) as f:
+        weight = f.get_tensor("model.layers.0.mlp.down_proj.weight")
+        scale = f.get_tensor("model.layers.0.mlp.down_proj.weight_scale_inv")
+
+    # weight_dequant
+    weight_dequantized = weight_dequant(weight, scale)
+    print(f"weight_dequantized: {weight_dequantized.shape}")
+    N, K = weight_dequantized.shape
+    M = 6400
+    x = torch.randn(2 ,M, K, dtype=torch.bfloat16, device='cuda')
+    # x_quantized, scale_x = act_quant(x, block_size)
+    
+    # Calculate time for 1000 fp8_gemm
+    i = 10
+    flops_per_gemm = 2 * M * N * K
+    total_flops = i * flops_per_gemm
+    
+    x_quantized, scale_x = act_quant(x, block_size)
+    result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+    x_quantized, scale_x = act_quant(x, block_size)
+    result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+
+    
+    t0 = time.time()
+    torch.cuda.synchronize()
+    for i in range(i):
+        x_quantized, scale_x = act_quant(x, block_size)
+        result_fp8_gemm = fp8_gemm(x_quantized, scale_x, weight, scale)
+    torch.cuda.synchronize()
+    t1 = time.time()
+    
+    total_time = t1 - t0
+    tflops = total_flops / total_time / 1e12
+    print(f"total_time: {total_time}")
+    print(f"tflops: {tflops}")
+    
+
+    
+    
 if __name__ == "__main__":
     test_fp8_gemm_vs_torch_matmul()
     test_fp8_gemm_vs_torch_matmul_load()
+    test_fp8_gemm_tplops()
     
\ No newline at end of file
diff --git a/ktransformers/util/custom_gguf.py b/ktransformers/util/custom_gguf.py
index 26afd39..d054ad3 100644
--- a/ktransformers/util/custom_gguf.py
+++ b/ktransformers/util/custom_gguf.py
@@ -25,6 +25,7 @@ import os
 from enum import IntEnum
 import torch
 import KTransformersOps
+from .custom_loader import SafeTensorLoader
 
 class GGMLQuantizationType(IntEnum):
     F32     = 0
@@ -168,12 +169,15 @@ class GGUFLoader:
     gguf_path: str
     tensor_file_map: dict # {tensor_name: tensor_file_path}
     gguf_file_meta: dict
+    safetensor_loader: SafeTensorLoader
     def __init__(self, gguf_path: str):
         # Check dir exist
         if not os.path.exists(gguf_path):
             raise FileNotFoundError(f"GGUF dir not found: {gguf_path}")
         if os.path.isfile(gguf_path):
             gguf_path = os.path.dirname(gguf_path)
+
+        self.safetensor_loader = None
         
         self.tensor_info = {}
         self.gguf_path = gguf_path
@@ -181,7 +185,13 @@ class GGUFLoader:
         self.file_data_map = {}
         self.gguf_file_meta = {}
         self.tensor_device_map = {}
-        
+
+        # I know this is ugly, but I don't want to change the original code too much
+        # TODO: merge gguf load and other loads.
+        safetensor_loader = SafeTensorLoader(gguf_path)
+        if safetensor_loader.tensor_file_map:
+            self.safetensor_loader = safetensor_loader
+            return
         # Walk through all the .gguf files in the directory
         found_gguf = False
         for root, dirs, files in os.walk(gguf_path):
@@ -288,6 +298,13 @@ class GGUFLoader:
         itemsize = int(np.empty([], dtype = item_type).itemsize)
         return mmap_data[offset : offset + itemsize * item_count]
     
+    def get_undequanted_tensor_and_ggml_type(self, name):
+        t = self.tensor_info[name]
+        data = self.get_mmap_tensor(name)
+        ggml_type = t["ggml_type"]
+        data = torch.from_numpy(data)
+        return data, ggml_type
+
     def load_expert_tensor(self, name, data, expert_id, elements_per_expert, device = "gpu")->torch.Tensor:
         t = self.tensor_info[name]
         if device.lower() == "cpu":
diff --git a/ktransformers/util/custom_loader.py b/ktransformers/util/custom_loader.py
new file mode 100644
index 0000000..ecc09a0
--- /dev/null
+++ b/ktransformers/util/custom_loader.py
@@ -0,0 +1,86 @@
+import struct
+import warnings
+import numpy as np
+import re
+import numpy.typing as npt
+from typing import Sequence
+import os
+from enum import IntEnum
+import torch
+import KTransformersOps
+from safetensors import safe_open
+from ktransformers.ktransformers_ext.triton.fp8gemm import fp8_gemm, act_quant, weight_dequant
+from safetensors.torch import save_file
+
+class SafeTensorLoader:
+    tensor_file_map = {}
+    tensor_type_map = {}
+    file_handle_map = {}
+    
+    def __init__(self, file_path: str):
+        self.__load_tensor_file_map(file_path)
+
+    def __load_tensor_file_map(self, file_path: str):
+        # 处理传入路径，确保是文件夹路径
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"Path not found: {file_path}")
+        if os.path.isfile(file_path):
+            folder_path = os.path.dirname(file_path)
+        else:
+            folder_path = file_path
+
+        found_safetensor = False
+        for root, _, files in os.walk(folder_path):
+            files = sorted(files)
+            for file in files:
+                if file.endswith(".safetensors"):
+                    found_safetensor = True
+                    file_path = os.path.join(root, file)
+                    if file not in self.file_handle_map:
+                        try:
+                            handle = safe_open(file_path, framework="pt")
+                            self.file_handle_map[file] = handle
+                        except Exception as e:
+                            print(f"Error opening Safetensor file {file_path}: {e}")
+                            continue
+
+                    f = self.file_handle_map.get(file)
+                    if f is None:
+                        continue
+                    try:
+                        for key in f.keys():
+                            self.tensor_file_map[key] = file
+                    except Exception as e:
+                        print(f"Error reading Safetensor file {file_path}: {e}")
+
+        # if not found_safetensor:
+        #     raise FileNotFoundError(f"No Safetensor files found in {folder_path}")
+
+    def load_tensor(self, key: str, device: str="cpu"):
+        if key not in self.tensor_file_map:
+            raise KeyError(f"Key {key} not found in Safetensor files")
+        file = self.tensor_file_map[key]
+        f = self.file_handle_map.get(file)
+        if f is None:
+            raise FileNotFoundError(f"File {file} not found in Safetensor files")
+        tensor = f.get_tensor(key)
+        return tensor.to(device)
+
+    def close_all_handles(self):
+        for handle in self.file_handle_map.values():
+            handle.close()
+        self.file_handle_map.clear()
+
+    def load_dequantized_tensor(self, key:str, device: str="cpu"):
+        if key not in self.tensor_file_map:
+            raise KeyError(f"Key {key} not found in Safetensor files")
+        file = self.tensor_file_map[key]
+        f = self.file_handle_map.get(file)
+        if f is None:
+            raise FileNotFoundError(f"File {file} not found in Safetensor files")
+        tensor = f.get_tensor(key).to(device)
+        if key.endswith(".weight"):
+            if key[:-7] + ".weight_scale_inv" in self.tensor_file_map:
+                weight_scale_inv = f.get_tensor(key[:-7] + ".weight_scale_inv").to(device)
+                tensor = weight_dequant(tensor, weight_scale_inv)
+        return tensor.to(device)
\ No newline at end of file
diff --git a/ktransformers/util/utils.py b/ktransformers/util/utils.py
index 81d007c..1c21135 100644
--- a/ktransformers/util/utils.py
+++ b/ktransformers/util/utils.py
@@ -66,12 +66,23 @@ def load_cur_state_dict(module: nn.Module, gguf_loader: GGUFLoader, prefix: str
     for name, param in local_state.items():
         key = prefix + name
         translated_key = translate_name_to_gguf(key)
-        if translated_key in gguf_loader.tensor_file_map:
+        
+        # TODO: Merge all loader.
+        # I know this is ugly but lets do it for now.
+        if gguf_loader.safetensor_loader is not None:
+            load_dequantized_tensor = gguf_loader.safetensor_loader.load_dequantized_tensor
+            tensor_file_map = gguf_loader.safetensor_loader.tensor_file_map
+        else:
+            load_dequantized_tensor = gguf_loader.load_gguf_tensor
+            tensor_file_map = gguf_loader.tensor_file_map
+        
+        if translated_key in tensor_file_map:
             target_dtype = torch.get_default_dtype()
             device = get_device(translated_key[:translated_key.rfind(".")], gguf_loader.tensor_device_map)
             print(f"loading {translated_key} to {device}")
             torch.cuda.empty_cache() # To fit in 16G VRAM. By "wkGCaSS - 知乎 https://zhuanlan.zhihu.com/p/25491611225"
-            weights = gguf_loader.load_gguf_tensor(translated_key, device = device).to(dtype = target_dtype)
+            # weights = gguf_loader.load_gguf_tensor(translated_key, device = device).to(dtype = target_dtype)
+            weights = load_dequantized_tensor(translated_key, device=device).to(dtype=target_dtype)
             set_param(module, name, weights)
             del weights
         else:
diff --git a/merge_tensors/merge_safetensor_gguf.py b/merge_tensors/merge_safetensor_gguf.py
new file mode 100644
index 0000000..7aeb62d
--- /dev/null
+++ b/merge_tensors/merge_safetensor_gguf.py
@@ -0,0 +1,214 @@
+# this script targets to merge the fp8 safe tensor and the gguf quantized tensors.
+
+import os
+# insert the path of the project
+import sys
+sys.path.insert(0, "/home/azure/ktransformers")
+import argparse
+import torch
+from ktransformers.util.custom_gguf import GGUFLoader, translate_name_to_gguf
+from safetensors import safe_open
+from safetensors.torch import save_file
+import re
+from collections import defaultdict
+
+def read_safetensor_keys_from_folder(folder_path)->dict:
+    """    
+    :param folder_path: folder path
+    :return: key_to_file_map
+    """
+    # check if the folder path is exist
+    if not os.path.exists(folder_path):
+        raise FileNotFoundError(f"GGUF dir not found: {folder_path}")
+    if os.path.isfile(folder_path):
+        folder_path = os.path.dirname(folder_path)
+    
+    key_to_file_map = {}
+
+    found_safetensor = False
+    for root, dirs, files in os.walk(folder_path):
+        # sort files
+        files = sorted(files)
+        for file in files:
+            if file.endswith(".safetensors"):
+                found_safetensor = True
+                file_path = os.path.join(root, file)
+                try:
+                    with safe_open(file_path, framework="pt") as f:
+                        for key in f.keys():
+                            if "model.layers.61" in key:
+                                # skip MTP layer
+                                continue
+                            # try:
+                            #     if int(key.split('.')[2]) > 4:
+                            #         continue
+                            # except:
+                            #     pass
+                            key_to_file_map[key] = file_path
+                except Exception as e:
+                    print(f"Error reading Safetensor file {file_path}: {e}")
+    
+    if not found_safetensor:
+        raise FileNotFoundError(f"No Safetensor files found in {folder_path}")
+    
+    return key_to_file_map
+
+tensor_from_gguf = [] # todo: add keys in gguf that should be used in the final tensor
+
+def translate_name(name:str)->str:
+    """
+    :param name: name of the tensor
+    :return: translated name
+    """
+    name = translate_name_to_gguf(name)
+    name = name.replace(".up_proj.", ".ffn_up_exps.")
+    name = name.replace(".down_proj.", ".ffn_down_exps.")
+    name = name.replace(".gate_proj.", ".ffn_gate_exps.")
+    name = name.replace(".ffn_gate_inp.e_score_correction_bias", ".exp_probs_b.bias") 
+    return name
+    
+
+def combine_tensor_sources(safetensor_path:str, gguf_path:str):
+    gguf_loader = GGUFLoader(gguf_path)
+    gguf_tensor_file_map = gguf_loader.tensor_file_map
+    safetensor_tensor_file_map = read_safetensor_keys_from_folder(safetensor_path)
+    
+    # build a map for the key to the tensor
+    # according to the key, we can get the tensor from the file
+    
+    target_tensor_map = {}
+    for key in safetensor_tensor_file_map.keys():
+        # for all experts, we use the gguf tensor
+        if ".mlp.experts." in key:
+            if '.weight_scale_inv' in key:
+                continue
+            key = '.'.join(key.split('.')[:5]+key.split('.')[-2:])
+            translated_key = translate_name(key)
+            target_tensor_map[key] = gguf_tensor_file_map[translated_key]
+            continue
+        
+        if any(target_key in key for target_key in tensor_from_gguf):
+            target_tensor_map[key] = gguf_tensor_file_map[translate_name(key)]
+        else:
+            target_tensor_map[key] = safetensor_tensor_file_map[key]
+    
+    return target_tensor_map, gguf_loader
+
+def write_combined_tensor(target_tensor_map: dict, output_path: str, gguf_loader: GGUFLoader):
+    # Ensure output directory exists
+    os.makedirs(output_path, exist_ok=True)
+    
+    # Cache for safetensor file handles and GGUF loaders
+    safetensors_cache = {}
+    gguf_cache = {}
+    
+    # Group tensors by layer
+    layer_groups = defaultdict(list)
+    non_layer_keys = []
+    layer_pattern = re.compile(r'\.layers\.(\d+)\.')
+    
+    for key in target_tensor_map:
+        match = layer_pattern.search(key)
+        if match:
+            layer_num = int(match.group(1))
+            layer_groups[layer_num].append(key)
+        else:
+            non_layer_keys.append(key)
+    
+    # Calculate total shards
+    total_shards = len(layer_groups) + (1 if non_layer_keys else 0) - 1
+    if total_shards == 0:
+        raise ValueError("No tensors to save")
+    
+    shard_idx = 0
+    
+    # Save non-layer tensors to the first shard if they exist
+    if non_layer_keys:
+        tensors = {}
+        for key in non_layer_keys:
+            file_path = target_tensor_map[key]
+            tensor = None
+            ggml_type = None
+            if file_path.endswith('.safetensors'):
+                if file_path not in safetensors_cache:
+                    safetensors_cache[file_path] = safe_open(file_path, framework='pt')
+                f = safetensors_cache[file_path]
+                tensor = f.get_tensor(key)
+            elif file_path.endswith('.gguf'):
+                gguf_name = translate_name(key)
+                tensor, ggml_type = gguf_loader.get_undequanted_tensor_and_ggml_type(gguf_name)
+            else:
+                raise ValueError(f"Unsupported file format: {file_path}")
+            tensors[translate_name(key)] = tensor
+            if ggml_type:
+                ggml_type = torch.tensor(ggml_type)
+                ggml_key = translate_name(key)[:-7] + ".ggml_type" if translate_name(key).endswith(".weight") else translate_name(key) + ".ggml_type"
+                tensors[ggml_key] = ggml_type
+        
+        output_file = os.path.join(output_path, f"model-{shard_idx:05}-of-{total_shards:05}.safetensors")
+        print(f"Saving non-layer tensors to {output_file}")
+        save_file(tensors, output_file)
+        print(tensors.keys())
+
+        shard_idx += 1
+    
+    # Save each layer's tensors to subsequent shards
+    for layer_num in sorted(layer_groups.keys()):
+        layer_keys = layer_groups[layer_num]
+        tensors = {}
+        for key in layer_keys:
+            file_path = target_tensor_map[key]
+            tensor = None
+            ggml_type = None
+            if file_path.endswith('.safetensors'):
+                if file_path not in safetensors_cache:
+                    safetensors_cache[file_path] = safe_open(file_path, framework='pt')
+                f = safetensors_cache[file_path]
+                tensor = f.get_tensor(key)
+                tensor_info = tensor.shape
+            elif file_path.endswith('.gguf'):
+                gguf_name = translate_name(key)
+                tensor, ggml_type = gguf_loader.get_undequanted_tensor_and_ggml_type(gguf_name)
+                # tensor_info = gguf_loader.tensor_info[gguf_name]
+                # ggml_type = gguf_loader.tensor_info[gguf_name]['ggml_type']
+            else:
+                raise ValueError(f"Unsupported file format: {file_path}")
+            tensors[translate_name(key)] = tensor
+            if ggml_type:
+                ggml_type = torch.tensor(ggml_type)
+                ggml_key = translate_name(key)[:-7] + ".ggml_type" if translate_name(key).endswith(".weight") else translate_name(key) + ".ggml_type"
+                tensors[ggml_key] = ggml_type
+        
+        output_file = os.path.join(output_path, f"model-{shard_idx:05}-of-{total_shards:05}.safetensors")
+        print(f"Saving layer {layer_num} to {output_file}")
+        print(tensors.keys())
+        save_file(tensors, output_file)
+        shard_idx += 1
+    
+    return
+    
+def main():
+    # 创建命令行参数解析器
+    parser = argparse.ArgumentParser(description="Read parameters from Safetensor and GGUF files")
+    parser.add_argument("--safetensor_path", type=str, help="Path to the Safetensor file", default="/mnt/data/model/DeepSeek-V3")
+    parser.add_argument("--gguf_path", type=str, help="Path to the GGUF file", default="/mnt/data/model/DeepseekV3-q4km-gguf")
+    parser.add_argument("--output_path", type=str, help="Path to the output file", default="/mnt/data/model/ktrans-safetensors/DeepSeek-V3-q4km-fp8")
+    
+    # print all the arguments
+    print("All the arguments:")
+    print(parser.parse_args())
+    
+    # 解析命令行参数
+    args = parser.parse_args()
+
+    safetensor_path = args.safetensor_path
+    gguf_path = args.gguf_path
+    output_path = args.output_path
+    
+    target_tensor_map, gguf_loader = combine_tensor_sources(safetensor_path, gguf_path)
+    write_combined_tensor(target_tensor_map, output_path, gguf_loader)
+    
+    return
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file

From 4dc5518e4d2ae89a687709bcbe05d2f3f80e00ad Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Mon, 24 Feb 2025 15:37:01 +0000
Subject: [PATCH 04/14] update fp8 kernel tutorial

---
 doc/SUMMARY.md                                |  3 +-
 doc/en/FAQ.md                                 |  5 ++-
 doc/en/injection_tutorial.md                  |  1 +
 .../server/backend/interfaces/transformers.py |  2 +-
 merge_tensors/README.md                       | 36 +++++++++++++++++++
 merge_tensors/merge_safetensor_gguf.py        |  1 -
 requirements-local_chat.txt                   |  3 +-
 7 files changed, 46 insertions(+), 5 deletions(-)
 create mode 100644 merge_tensors/README.md

diff --git a/doc/SUMMARY.md b/doc/SUMMARY.md
index 4efb7ea..47b4a02 100644
--- a/doc/SUMMARY.md
+++ b/doc/SUMMARY.md
@@ -5,10 +5,11 @@
 - [Installation Guide](en/install.md)
 
 # Tutorial 
-- [Deepseek-R1/V3 Show Case](en/DeepseekR1_V3_tutorial.md)
+- [Deepseek-R1/V3 Show Case/Tutorial](en/DeepseekR1_V3_tutorial.md)
 - [Why KTransformers So Fast](en/deepseek-v2-injection.md)
 - [Injection Tutorial](en/injection_tutorial.md)
 - [Multi-GPU Tutorial](en/multi-gpu-tutorial.md)
+- [Using FP8 GPU Kernel](../merge_tensors/README.md)
 # Server
   - [Server](en/api/server/server.md)
   - [Website](en/api/server/website.md)
diff --git a/doc/en/FAQ.md b/doc/en/FAQ.md
index 51c6271..93977cf 100644
--- a/doc/en/FAQ.md
+++ b/doc/en/FAQ.md
@@ -55,7 +55,7 @@ You have to set `--cpu_infer` to the number of cores you want to use. The more c
 
 ### Q: My DeepSeek-R1 model is not thinking.
 
-According to DeepSeek, you need to enforce the model to initiate its response with "\<think>\n" at the beginning of every output by passing the arg `--force_think true `.
+According to DeepSeek, you need to enforce the model to initiate its response with "\<think>\n" at the beginning of every output by passing the arg `--force_think True `.
 
 ### Q: Loading gguf error
 
@@ -63,9 +63,12 @@ Make sure you:
 1. Have the `gguf` file in the `--gguf_path` directory.
 2. The directory only contains gguf files from one model. If you have multiple models, you need to separate them into different directories.
 3. The folder name it self should not end with `.gguf`, eg. `Deep-gguf` is correct, `Deep.gguf` is wrong.
+4. The file itself is not corrupted; you can verify this by checking that the sha256sum matches the one from huggingface, modelscope, or hf-mirror.
 
 ### Q: Version `GLIBCXX_3.4.30' not found
 The detailed error:
 >ImportError: /mnt/data/miniconda3/envs/xxx/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.30' not found (required by /home/xxx/xxx/ktransformers/./cpuinfer_ext.cpython-312-x86_64-linux-gnu.so)
 
 Running `conda install -c conda-forge libstdcxx-ng` can solve the problem.
+
+
diff --git a/doc/en/injection_tutorial.md b/doc/en/injection_tutorial.md
index 5ebb327..4518836 100644
--- a/doc/en/injection_tutorial.md
+++ b/doc/en/injection_tutorial.md
@@ -59,6 +59,7 @@ Supported operators and their corresponding classes are as follows:
 | Linear    | KTransformersLinear    | KLinearMarlin           | Marlin as backend    |
 |           |                        | KLinearTorch            | pytorch as backend   |
 |           |                        | KLinearCPUInfer         | llamafile as backend |
+|           |                        | KLinearFP8         | Triton fp8_gemm kernel. Requires GPU be able to caluculate fp8 data |
 | experts   | KTransformersExperts   | KExpertsTorch           | pytorch as backend   |
 |           |                        | KExpertsMarlin          | Marlin as backend    |
 |           |                        | KExpertsCPU             | llamafile as backend |
diff --git a/ktransformers/server/backend/interfaces/transformers.py b/ktransformers/server/backend/interfaces/transformers.py
index 8211933..c6f8e3a 100644
--- a/ktransformers/server/backend/interfaces/transformers.py
+++ b/ktransformers/server/backend/interfaces/transformers.py
@@ -340,7 +340,7 @@ class TransformersInterface(BackendInterfaceBase):
                                              sm_scale=(self.model.config.qk_rope_head_dim + self.model.config.qk_nope_head_dim) ** (-0.5), q_data_type=torch.bfloat16, kv_data_type=torch.bfloat16)
                 next_token = self.decode_one_tokens()
                 self.profiler.inc("decode")
-                if next_token == self.tokenizer.eos_token_id:
+                if next_token == self.tokenizer.eos_token_id or "<|im_end|>" == self.tokenizer.decode(next_token):
                     assert self.args.batch_size == 1
                     break
                 yield self.append_new_tokens(next_token)
diff --git a/merge_tensors/README.md b/merge_tensors/README.md
new file mode 100644
index 0000000..c786197
--- /dev/null
+++ b/merge_tensors/README.md
@@ -0,0 +1,36 @@
+# FP8 Linear Kernel.
+For DeepSeek-R1/V3, the DeepSeek-AI team provides fp8 safetensors. We have integrated the FP8 GPU kernel into the KTransformers. But to keep the experts still in CPU to save GPU memory, we still use ggml(GGUF tensors) quantization for experts. In this way, we can increase the precision in calculating attention, which may improve the model's performance.
+
+Therefore, to use fp8 linear kernel, we need to merge fp8 weights and gguf files. We have provides prepared weights in huggingface so that you can use them directly. 
+
+[KVCache-ai/DeepSeek-V3](https://huggingface.co/KVCache-ai/DeepSeek-V3/upload/main)
+
+
+If you want to use other formats of ggml quantization, you can use the following script to merge them.
+
+## Example
+To use fp8 linear kernal and q4km experts.
+```shell
+bash
+python convert_model.py \
+  --safetensor_path <fp8 safetensor path> \
+  --gguf_path <q4km gguf folder path> \
+  --output_path <output path>
+```
+* `--safetensor_path`:	input path of safetensor file
+* `--gguf_path`: input path of gguf folder
+* `--output_path`: output path of merged file
+
+
+## To Run DeepSeek-V3 with fp8 linear kernel and q4km experts
+
+
+```shell
+python ktransformers/local_chat.py --model_path deepseek-ai/DeepSeek-V3 --gguf_path <new weights folder> --optimize_rule_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml --cpu_infer <cpu cores + 1>
+```
+
+
+> NOTES: 
+> 1. Using fp8 linear kernel and q4km experts will consume approximatly 19GB GPU memory. 
+> 2. I know the the new way to load module is ugly, we are working on it.
+> 3. Though the model is a mixture of fp8 and ggml, they are stored in .safetensor files. Please pass the folder path of the new weights to `--gguf_path`.
diff --git a/merge_tensors/merge_safetensor_gguf.py b/merge_tensors/merge_safetensor_gguf.py
index 7aeb62d..67e09e5 100644
--- a/merge_tensors/merge_safetensor_gguf.py
+++ b/merge_tensors/merge_safetensor_gguf.py
@@ -3,7 +3,6 @@
 import os
 # insert the path of the project
 import sys
-sys.path.insert(0, "/home/azure/ktransformers")
 import argparse
 import torch
 from ktransformers.util.custom_gguf import GGUFLoader, translate_name_to_gguf
diff --git a/requirements-local_chat.txt b/requirements-local_chat.txt
index 0479d36..bf4df63 100644
--- a/requirements-local_chat.txt
+++ b/requirements-local_chat.txt
@@ -4,4 +4,5 @@ numpy
 torch>=2.3.0
 packaging
 cpufeature
-protobuf
\ No newline at end of file
+protobuf
+tiktoken
\ No newline at end of file

From 36fbeee341e283a93b6befa2a4d9085b7a5dd2b1 Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Tue, 25 Feb 2025 08:21:18 +0000
Subject: [PATCH 05/14] Update doc

---
 README.md                                     |  5 +-
 README_ZH.md                                  | 17 +++--
 doc/SUMMARY.md                                |  2 +-
 doc/en/FAQ.md                                 |  2 +-
 doc/en/fp8_kernel.md                          | 74 +++++++++++++++++++
 doc/en/install.md                             |  2 +-
 ktransformers/local_chat.py                   | 10 +--
 .../backend/interfaces/ktransformers.py       |  6 +-
 merge_tensors/README.md                       | 36 ---------
 merge_tensors/merge_safetensor_gguf.py        |  3 +-
 requirements-local_chat.txt                   |  3 +-
 11 files changed, 101 insertions(+), 59 deletions(-)
 create mode 100644 doc/en/fp8_kernel.md
 delete mode 100644 merge_tensors/README.md

diff --git a/README.md b/README.md
index 30f1425..f62528b 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,8 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
 
 <h2 id="Updates">🔥 Updates</h2>
 
-* **Feb 15, 2025**: KTransformers V0.2.1: Longer Context (from 4K to 8K for 24GB VRAM) & Slightly Faster Speed （+15%) (Up to 16 Tokens/s), update docs [here](./doc/en/DeepseekR1_V3_tutorial.md) and [online books](https://kvcache-ai.github.io/ktransformers/).
+* **Feb 25, 2025**: Support [FP8 GPU kernel](./doc/en/fp8_kernel.md) for DeepSeek-V3 and R1; Longer Context (from 8K to 128K for 24GB VRAM).
+* **Feb 15, 2025**: Longer Context (from 4K to 8K for 24GB VRAM) & Slightly Faster Speed （+15%, up to 16 Tokens/s), update [docs](./doc/en/DeepseekR1_V3_tutorial.md) and [online books](https://kvcache-ai.github.io/ktransformers/).
 * **Feb 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see [here](./doc/en/DeepseekR1_V3_tutorial.md).
 * **Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
 * **Aug 15, 2024**: Update detailed [tutorial](doc/en/injection_tutorial.md) for injection and multi-GPU. 
@@ -125,7 +126,7 @@ To utilize the provided kernels, users only need to create a YAML-based injectio
 ```python
 with torch.device("meta"):
     model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
-optimize_and_load_gguf(model, optimize_rule_path, gguf_path, config)
+optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
 ...
 generated = prefill_and_generate(model, tokenizer, input_tensor.cuda(), max_new_tokens=1000)
 ```
diff --git a/README_ZH.md b/README_ZH.md
index 4cdd3c1..3696a08 100644
--- a/README_ZH.md
+++ b/README_ZH.md
@@ -21,7 +21,8 @@ KTransformers 是一个以 Python 为中心的灵活框架，其核心是可扩
 
 <h2 id="Updates">🔥 更新</h2>
 
-* **2025 年 2 月 15 日**：KTransformers V0.2.1: 长上下文(从4K到8K，24GB VRAM) & 稍快的速度(+15%)(最快 16 Tokens/s)，文档请参见 [这里](./doc/en/DeepseekR1_V3_tutorial.md) 和 [在线指南](https://kvcache-ai.github.io/ktransformers/) 。
+* **2025 年 2 月 15 日**：为DeepSeek-V3/R1支持[FP8 GPU内核](./doc/en/fp8_kernel.md); 支持更长的上下文 (从8K到128K仅用24GB VRAM).
+* **2025 年 2 月 15 日**：长上下文(从4K到8K，24GB VRAM) & 稍快的速度(+15%)(最快 16 Tokens/s)，文档请参见 [这里](./doc/en/DeepseekR1_V3_tutorial.md) 和 [在线指南](https://kvcache-ai.github.io/ktransformers/) 。
 * **2025 年 2 月 10 日**：支持 Deepseek-R1 和 V3 在单个（24GB VRAM）/多 GPU 和 382G DRAM 上运行，速度提升高达 3~28 倍。详细教程请参见 [这里](./doc/en/DeepseekR1_V3_tutorial.md)。
 * **2024 年 8 月 28 日**：支持 InternLM2.5-7B-Chat-1M 模型下的 1M 上下文，使用 24GB 的 VRAM 和 150GB 的 DRAM。详细教程请参见 [这里](./doc/en/long_context_tutorial.md)。
 * **2024 年 8 月 28 日**：将 DeepseekV2 所需的 VRAM 从 21G 降低到 11G。
@@ -68,11 +69,11 @@ https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c
 
 </p>
 
-<h3>在仅 24GB VRAM 的桌面上进行 1M 上下文本地推理</h3>
-<p align="center">
-
-https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12
+<!-- <h3>在仅 24GB VRAM 的桌面上进行 1M 上下文本地推理</h3>
+<p align="center"> -->
 
+<!-- https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12 -->
+<!-- 
 * **1M 上下文 InternLM 2.5 7B**：以全 bf16 精度运行，使用 24GB VRAM 和 150GB DRAM，可在本地桌面设置中实现。在 1M "针在干草堆中" 测试中达到 92.88% 的成功率，在 128K NIAH 测试中达到 100%。
 
 <p align="center">
@@ -89,7 +90,7 @@ https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12
 
 * **增强的速度**：使用稀疏注意力，通过 llamafile 内核实现 1M 上下文生成 16.91 tokens/s 的速度。这种方法比 llama.cpp 的全注意力方法快 10 倍以上。
 
-* **灵活的稀疏注意力框架**：提供了一个灵活的块稀疏注意力框架，用于 CPU 卸载解码。与 SnapKV、Quest 和 InfLLm 兼容。更多信息请参见 [这里](./doc/en/long_context_introduction.md)。
+* **灵活的稀疏注意力框架**：提供了一个灵活的块稀疏注意力框架，用于 CPU 卸载解码。与 SnapKV、Quest 和 InfLLm 兼容。更多信息请参见 [这里](./doc/en/long_context_introduction.md)。 -->
 
 <strong>更多高级功能即将推出，敬请期待！</strong>
 
@@ -116,7 +117,7 @@ KTransformers 的核心是一个用户友好的、基于模板的注入框架。
 ```python
 with torch.device("meta"):
     model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
-optimize_and_load_gguf(model, optimize_rule_path, gguf_path, config)
+optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
 ...
 generated = prefill_and_generate(model, tokenizer, input_tensor.cuda(), max_new_tokens=1000)
 ```
@@ -151,7 +152,7 @@ YAML 文件中的每个规则都有两部分：`match` 和 `replace`。`match` 
 
 <h2 id="ack">致谢和贡献者</h2>
 
-KTransformer 的开发基于 Transformers 提供的灵活和多功能框架。我们还受益于 GGUF/GGML、Llamafile 和 Marlin 等高级内核。我们计划通过向上游贡献我们的修改来回馈社区。
+KTransformer 的开发基于 Transformers 提供的灵活和多功能框架。我们还受益于 GGUF/GGML、Llamafile 、 Marlin、sglang和flashinfer 等高级内核。我们计划通过向上游贡献我们的修改来回馈社区。
 
 KTransformer 由清华大学 <a href="https://madsys.cs.tsinghua.edu.cn/">MADSys group</a> 小组的成员以及 <a href="http://approaching.ai/">Approaching.AI</a> 的成员积极维护和开发。我们欢迎新的贡献者加入我们，使 KTransformer 更快、更易于使用。
 
diff --git a/doc/SUMMARY.md b/doc/SUMMARY.md
index 47b4a02..94172ce 100644
--- a/doc/SUMMARY.md
+++ b/doc/SUMMARY.md
@@ -9,7 +9,7 @@
 - [Why KTransformers So Fast](en/deepseek-v2-injection.md)
 - [Injection Tutorial](en/injection_tutorial.md)
 - [Multi-GPU Tutorial](en/multi-gpu-tutorial.md)
-- [Using FP8 GPU Kernel](../merge_tensors/README.md)
+- [Use FP8 GPU Kernel](en/fp8_kernel.md)
 # Server
   - [Server](en/api/server/server.md)
   - [Website](en/api/server/website.md)
diff --git a/doc/en/FAQ.md b/doc/en/FAQ.md
index 93977cf..c8f58a2 100644
--- a/doc/en/FAQ.md
+++ b/doc/en/FAQ.md
@@ -45,7 +45,7 @@ from-https://github.com/kvcache-ai/ktransformers/issues/129#issue-2842799552
 
 ### Q: If I don't have enough VRAM, but I have multiple GPUs, how can I utilize them?
 
-Use the `--optimize_rule_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml` to load the two optimized rule yaml file. You may also use it as an example to write your own 4/8 gpu optimized rule yaml file.
+Use the `--optimize_config_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu.yaml` to load the two optimized rule yaml file. You may also use it as an example to write your own 4/8 gpu optimized rule yaml file.
 
 > Note: The ktransformers' multi-gpu stratigy is pipline, which is not able to speed up the model's inference. It's only for the model's weight distribution.
 
diff --git a/doc/en/fp8_kernel.md b/doc/en/fp8_kernel.md
new file mode 100644
index 0000000..f1d86c3
--- /dev/null
+++ b/doc/en/fp8_kernel.md
@@ -0,0 +1,74 @@
+# FP8 Linear Kernel for DeepSeek-V3
+
+## Overview
+The DeepSeek-AI team provides FP8 safetensors for DeepSeek-R1/V3 models. We achieve performance optimization through the following works:
+- **FP8 GPU Kernel Integration**: FP8 linear layer acceleration kernels integrated in KTransformers
+- **Hybrid Quantization Architecture**:
+  - Attention and Shared-Expert modules use FP8 precision (enhances computational accuracy)
+  - Experts modules retain GGML quantization (GGUF format, reside in CPU to save GPU memory)
+
+So those who are persuing the best performance can use the FP8 linear kernel for DeepSeek-V3/R1.
+
+## Key Features
+✅ Hybrid Precision Architecture (FP8 + GGML)
+✅ Memory Optimization (~19GB VRAM usage)
+
+## Quick Start
+### Using Pre-Merged Weights
+
+Pre-merged weights are available on Hugging Face:
+[KVCache-ai/DeepSeek-V3](https://huggingface.co/KVCache-ai/DeepSeek-V3)
+[KVCache-ai/DeepSeek-R1](https://huggingface.co/KVCache-ai/DeepSeek-R1)
+> Please confirm the weights are fully uploaded before downloading. The large file size may extend Hugging Face upload time.
+
+
+Download Pre-Merged Weights
+```shell
+pip install -U huggingface_hub
+
+# Optional: Use HF Mirror for faster downloads in special area.
+# export HF_ENDPOINT=https://hf-mirror.com 
+
+huggingface-cli download --resume-download KVCache-ai/DeepSeek-V3 --local-dir <local_dir>
+```
+### Using merge scripts
+If you got local DeepSeek-R1/V3 fp8 safetensors and q4km gguf weights, you can merge them using the following scripts.
+
+```shell
+python convert_model.py \
+  --safetensor_path <fp8_safetensor_path> \
+  --gguf_path <q4km_gguf_folder_path> \
+  --output_path <merged_output_path>
+```
+
+* `--safetensor_path`:	input path of safetensor file([Download](https://huggingface.co/deepseek-ai/DeepSeek-V3/tree/main)).
+* `--gguf_path`: input path of gguf folder ([Download](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M)).
+* `--output_path`: output path of merged file.
+
+
+### Execution Notes
+
+Launch local_chat.py with custom quantized experts
+```shell
+python ktransformers/local_chat.py \
+  --model_path deepseek-ai/DeepSeek-V3 \
+  --gguf_path <merged_weights_folder> \
+  --optimize_config_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml \
+  --cpu_infer <cpu_cores + 1>
+```
+
+
+## Notes
+
+⚠️ Hardware Requirements
+* Recommended minimum 19GB available VRAM for FP8 kernel.
+* Requires GPU with FP8 support (e.g., 4090)
+
+⏳ First-Run Optimization
+JIT compilation causes longer initial execution (subsequent runs retain optimized speed).
+
+🔄 Temporary Interface
+Current weight loading implementation is provisional - will be refined in future versions
+
+📁 Path Specification
+Despite hybrid quantization, merged weights are stored as .safetensors - pass the containing folder path to `--gguf_path`
\ No newline at end of file
diff --git a/doc/en/install.md b/doc/en/install.md
index 269e8fb..2a4a6af 100644
--- a/doc/en/install.md
+++ b/doc/en/install.md
@@ -141,7 +141,7 @@ It features the following arguments:
 
 - `--gguf_path` (required): Path of a directory containing GGUF files which could that can be downloaded from [Hugging Face](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main). Note that the directory should only contains GGUF of current model, which means you need one separate directory for each model.
 
-- `--optimize_rule_path` (required except for Qwen2Moe and DeepSeek-V2): Path of YAML file containing optimize rules. There are two rule files pre-written in the [ktransformers/optimize/optimize_rules](ktransformers/optimize/optimize_rules) directory for optimizing DeepSeek-V2 and Qwen2-57B-A14, two SOTA MoE models.
+- `--optimize_config_path` (required except for Qwen2Moe and DeepSeek-V2): Path of YAML file containing optimize rules. There are two rule files pre-written in the [ktransformers/optimize/optimize_rules](ktransformers/optimize/optimize_rules) directory for optimizing DeepSeek-V2 and Qwen2-57B-A14, two SOTA MoE models.
 
 - `--max_new_tokens`: Int (default=1000). Maximum number of new tokens to generate.
 
diff --git a/ktransformers/local_chat.py b/ktransformers/local_chat.py
index d5e74de..920a3f6 100644
--- a/ktransformers/local_chat.py
+++ b/ktransformers/local_chat.py
@@ -54,7 +54,7 @@ default_optimize_rules = {
 
 def local_chat(
     model_path: str | None = None,
-    optimize_rule_path: str = None,
+    optimize_config_path: str = None,
     gguf_path: str | None = None,
     max_new_tokens: int = 300,
     cpu_infer: int = Config().cpu_infer,
@@ -95,12 +95,12 @@ def local_chat(
                 config, trust_remote_code=True, attn_implementation="flash_attention_2"
             )
 
-    if optimize_rule_path is None:
+    if optimize_config_path is None:
         if config.architectures[0] in default_optimize_rules:
             print("using default_optimize_rule for", config.architectures[0])
-            optimize_rule_path = default_optimize_rules[config.architectures[0]]
+            optimize_config_path = default_optimize_rules[config.architectures[0]]
         else:
-            optimize_rule_path = input(
+            optimize_config_path = input(
                 "please input the path of your rule file(yaml file containing optimize rules):"
             )
 
@@ -108,7 +108,7 @@ def local_chat(
         gguf_path = input(
             "please input the path of your gguf file(gguf file in the dir containing input gguf file must all belong to current model):"
         )
-    optimize_and_load_gguf(model, optimize_rule_path, gguf_path, config)
+    optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
     
     try:
             model.generation_config = GenerationConfig.from_pretrained(model_path)
diff --git a/ktransformers/server/backend/interfaces/ktransformers.py b/ktransformers/server/backend/interfaces/ktransformers.py
index 49a3f16..55e32de 100644
--- a/ktransformers/server/backend/interfaces/ktransformers.py
+++ b/ktransformers/server/backend/interfaces/ktransformers.py
@@ -35,9 +35,9 @@ class KTransformersInterface(TransformersInterface):
         with torch.device("meta"):
             self.model = custom_models[config.architectures[0]](config)
         if default_args.optimize_config_path is None:
-            optimize_rule_path = default_optimize_rules[config.architectures[0]]
+            optimize_config_path = default_optimize_rules[config.architectures[0]]
         else:
-            optimize_rule_path = args.optimize_config_path
+            optimize_config_path = args.optimize_config_path
 
         # print(optimize_config)
 
@@ -47,7 +47,7 @@ class KTransformersInterface(TransformersInterface):
                 "please input the path of your gguf file(gguf file in the dir containing input gguf file must all"
                 " belong to current model):"
             )
-        optimize_and_load_gguf(self.model, optimize_rule_path, gguf_path, config)
+        optimize_and_load_gguf(self.model, optimize_config_path, gguf_path, config)
 
         self.device_map = self.model.gguf_loader.tensor_device_map
         # logger.info(f"{args.model_name} loaded from {args.model_dir} to {self.device_map}")
diff --git a/merge_tensors/README.md b/merge_tensors/README.md
deleted file mode 100644
index c786197..0000000
--- a/merge_tensors/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# FP8 Linear Kernel.
-For DeepSeek-R1/V3, the DeepSeek-AI team provides fp8 safetensors. We have integrated the FP8 GPU kernel into the KTransformers. But to keep the experts still in CPU to save GPU memory, we still use ggml(GGUF tensors) quantization for experts. In this way, we can increase the precision in calculating attention, which may improve the model's performance.
-
-Therefore, to use fp8 linear kernel, we need to merge fp8 weights and gguf files. We have provides prepared weights in huggingface so that you can use them directly. 
-
-[KVCache-ai/DeepSeek-V3](https://huggingface.co/KVCache-ai/DeepSeek-V3/upload/main)
-
-
-If you want to use other formats of ggml quantization, you can use the following script to merge them.
-
-## Example
-To use fp8 linear kernal and q4km experts.
-```shell
-bash
-python convert_model.py \
-  --safetensor_path <fp8 safetensor path> \
-  --gguf_path <q4km gguf folder path> \
-  --output_path <output path>
-```
-* `--safetensor_path`:	input path of safetensor file
-* `--gguf_path`: input path of gguf folder
-* `--output_path`: output path of merged file
-
-
-## To Run DeepSeek-V3 with fp8 linear kernel and q4km experts
-
-
-```shell
-python ktransformers/local_chat.py --model_path deepseek-ai/DeepSeek-V3 --gguf_path <new weights folder> --optimize_rule_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-fp8-linear-ggml-experts.yaml --cpu_infer <cpu cores + 1>
-```
-
-
-> NOTES: 
-> 1. Using fp8 linear kernel and q4km experts will consume approximatly 19GB GPU memory. 
-> 2. I know the the new way to load module is ugly, we are working on it.
-> 3. Though the model is a mixture of fp8 and ggml, they are stored in .safetensor files. Please pass the folder path of the new weights to `--gguf_path`.
diff --git a/merge_tensors/merge_safetensor_gguf.py b/merge_tensors/merge_safetensor_gguf.py
index 67e09e5..69780fe 100644
--- a/merge_tensors/merge_safetensor_gguf.py
+++ b/merge_tensors/merge_safetensor_gguf.py
@@ -3,6 +3,7 @@
 import os
 # insert the path of the project
 import sys
+sys.path.insert(0, "/home/azure/ktransformers")
 import argparse
 import torch
 from ktransformers.util.custom_gguf import GGUFLoader, translate_name_to_gguf
@@ -180,7 +181,7 @@ def write_combined_tensor(target_tensor_map: dict, output_path: str, gguf_loader
         
         output_file = os.path.join(output_path, f"model-{shard_idx:05}-of-{total_shards:05}.safetensors")
         print(f"Saving layer {layer_num} to {output_file}")
-        print(tensors.keys())
+        # print(tensors.keys())
         save_file(tensors, output_file)
         shard_idx += 1
     
diff --git a/requirements-local_chat.txt b/requirements-local_chat.txt
index bf4df63..ad280c0 100644
--- a/requirements-local_chat.txt
+++ b/requirements-local_chat.txt
@@ -5,4 +5,5 @@ torch>=2.3.0
 packaging
 cpufeature
 protobuf
-tiktoken
\ No newline at end of file
+tiktoken
+blobfile
\ No newline at end of file

From f4c198bd42f037ccc570eb8f1f1b4ab1ea9c7fa2 Mon Sep 17 00:00:00 2001
From: Atream <boxin-zhang@qq.com>
Date: Tue, 25 Feb 2025 08:52:02 +0000
Subject: [PATCH 06/14] support absorb for prefill long context

---
 ktransformers/local_chat.py                   |  4 +-
 ktransformers/operators/attention.py          | 52 +++++++++++++------
 ktransformers/operators/flashinfer_wrapper.py | 30 ++++++++---
 ktransformers/operators/models.py             |  6 ++-
 .../optimize_rules/DeepSeek-V3-Chat.yaml      |  1 +
 .../backend/interfaces/ktransformers.py       |  5 ++
 .../server/backend/interfaces/transformers.py |  2 +-
 ktransformers/util/utils.py                   | 26 ++++++++--
 8 files changed, 93 insertions(+), 33 deletions(-)

diff --git a/ktransformers/local_chat.py b/ktransformers/local_chat.py
index d087752..5e57a22 100644
--- a/ktransformers/local_chat.py
+++ b/ktransformers/local_chat.py
@@ -28,7 +28,7 @@ from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
 from ktransformers.models.modeling_deepseek_v3 import DeepseekV3ForCausalLM
 from ktransformers.models.modeling_llama import LlamaForCausalLM
 from ktransformers.models.modeling_mixtral import MixtralForCausalLM
-from ktransformers.util.utils import prefill_and_generate
+from ktransformers.util.utils import prefill_and_generate, get_compute_capability
 from ktransformers.server.config.config import Config
 from ktransformers.operators.flashinfer_wrapper import flashinfer_enabled
 
@@ -168,7 +168,7 @@ def local_chat(
             assert Config().long_context_config['max_seq_len'] > input_tensor.shape[1] + max_new_tokens, \
             "please change max_seq_len in  ~/.ktransformers/config.yaml"
         
-        if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or "DeepseekV3ForCausalLM") and flashinfer_enabled:
+        if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8:
             generated = prefill_and_generate(
                 model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think,
                 use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim
diff --git a/ktransformers/operators/attention.py b/ktransformers/operators/attention.py
index b4c5402..5e7391f 100644
--- a/ktransformers/operators/attention.py
+++ b/ktransformers/operators/attention.py
@@ -16,6 +16,7 @@ from ktransformers.models.modeling_deepseek import DeepseekV2Attention, apply_ro
 from typing import Optional, Tuple
 from ktransformers.operators.base_operator import BaseInjectedModule
 from ktransformers.util.custom_gguf import GGUFLoader
+from ktransformers.util.utils import get_compute_capability
 import logging
 from transformers.configuration_utils import PretrainedConfig
 from transformers.cache_utils import Cache
@@ -48,12 +49,14 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                  prefill_device: str = "cuda",
                  generate_device: str = "cuda",
                  chunck_size: int = 1000,
+                 absorb_for_prefill: bool = False,
                  **kwargs):
         BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs)
         self.orig_module.__init__(orig_module.config,
             orig_module.layer_idx)
         self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
         self.mla_wrapper = None
+        self.absorb_for_prefill = absorb_for_prefill
 
     def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
         if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
@@ -242,7 +245,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             q_nope = q_nope.transpose(1, 2) # q_len is 1, no GPU overhead, same below
             q_nope = torch.matmul(q_nope, q_absorb) # batched MM
             q_nope = q_nope.transpose(1, 2)
-            assert q_nope.is_contiguous()
+            #assert q_nope.is_contiguous()
             
             # q_nope [bsz, q_len, self.num_heads, self.kv_lora_rank]
             # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim]
@@ -282,6 +285,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             # out_absorb [self.num_heads, self.v_head_dim, self.kv_lora_rank]
             attn_output = attn_output.transpose(1, 2)
             attn_output = torch.matmul(attn_output, out_absorb.mT)
+            attn_output = attn_output.transpose(1, 2)
             
             attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)
             attn_output = self.o_proj(attn_output)
@@ -380,7 +384,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim] k_pe [bsz, q_len, 1, self.qk_rope_head_dim]
         
         # decode
-        if q_len == 1:
+        if q_len == 1 or self.absorb_for_prefill:
             if past_key_value is not None:
                 cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
                 compressed_kv_with_k_pe, page_table = past_key_value.update(compressed_kv, k_pe, self.layer_idx, cache_kwargs)
@@ -395,27 +399,41 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             q_nope = q_nope.transpose(1, 2) # q_len is 1, no GPU overhead, same below
             q_nope = torch.matmul(q_nope, q_absorb) # batched MM
             q_nope = q_nope.transpose(1, 2)
-            assert q_nope.is_contiguous()
+            q_nope = q_nope.contiguous()
+            #assert q_nope.is_contiguous()
             
             # q_nope [bsz, q_len, self.num_heads, self.kv_lora_rank]
             # q_pe [bsz, q_len, self.num_heads, self.qk_rope_head_dim]
-            q_nope.squeeze_(1)
-            q_pe.squeeze_(1)
+            q_nope.squeeze_(0)
+            q_pe.squeeze_(0)
 
             # flash attn doesn't support head_dim bigger than 256, use flashinfer
             if self.mla_wrapper is None:
                 self.mla_wrapper = MLAWrapperSingleton.get_instance(self.device, 1, past_key_value.max_pages, use_cuda_graph = True)
-                if self.mla_wrapper.need_plan:
-                    self.mla_wrapper.need_plan = False
+            if self.mla_wrapper.need_plan:
+                self.mla_wrapper.need_plan = False
+                if q_len == 1:
                     self.mla_wrapper.plan(None,None,None,
-                                      position_ids.squeeze(1)+1,
-                                      self.num_heads,
-                                      self.kv_lora_rank,
-                                      self.qk_rope_head_dim,
-                                      past_key_value.page_size,
-                                      self.softmax_scale,
-                                      q_nope.dtype,
-                                      compressed_kv.dtype)
+                                        position_ids.squeeze(1)+1,
+                                        self.num_heads,
+                                        self.kv_lora_rank,
+                                        self.qk_rope_head_dim,
+                                        past_key_value.page_size,
+                                        self.softmax_scale,
+                                        q_nope.dtype,
+                                        compressed_kv.dtype)
+                else:
+                    qo_indptr = torch.tensor([0, q_len], dtype=torch.int32, device=self.device)
+                    kv_len_arr = torch.tensor([position_ids[0, -1].item()+1], dtype=torch.int32, device=self.device)
+                    self.mla_wrapper.plan(qo_indptr,None,None,
+                                        kv_len_arr,
+                                        self.num_heads,
+                                        self.kv_lora_rank,
+                                        self.qk_rope_head_dim,
+                                        past_key_value.page_size,
+                                        self.softmax_scale,
+                                        q_nope.dtype,
+                                        compressed_kv.dtype)
             attn_output = self.mla_wrapper.run(q_nope, q_pe, compressed_kv, k_pe).view(bsz, q_len, self.num_heads, self.kv_lora_rank)
 
             """
@@ -443,6 +461,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             # out_absorb [self.num_heads, self.v_head_dim, self.kv_lora_rank]
             attn_output = attn_output.transpose(1, 2) # [bsz, self.num_heads, q_len, self.kv_lora_rank]
             attn_output = torch.matmul(attn_output, out_absorb.mT) # [bsz, self.num_heads, q_len, self.v_head_dim]
+            attn_output = attn_output.transpose(1, 2).contiguous() # [bsz, q_len, self.num_heads, self.kv_lora_rank]
             
             attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim) # [bsz, q_len, self.num_heads * self.v_head_dim]
             attn_output = self.o_proj(attn_output)
@@ -571,7 +590,8 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if os.name == 'nt':
+        if os.name == 'nt' or get_compute_capability()<8:
+            print("for Windows or GPU before ampere, use forward_windows")
             return self.forward_windows(
                 hidden_states,
                 attention_mask,
diff --git a/ktransformers/operators/flashinfer_wrapper.py b/ktransformers/operators/flashinfer_wrapper.py
index b3b9dd1..864b33e 100644
--- a/ktransformers/operators/flashinfer_wrapper.py
+++ b/ktransformers/operators/flashinfer_wrapper.py
@@ -9,7 +9,7 @@ flashinfer_enabled = False
 
 try:
     import flashinfer
-    flashinfer_enabled = False # disabled now, TODO:use new version of flashinfer and enable
+    flashinfer_enabled = True
     print("found flashinfer")
     
 except ImportError:
@@ -132,14 +132,14 @@ class MLAWrapper():
             head_dim_ckv,
             head_dim_kpe,
             page_size,
-            False, # causal is False for decoding
+            True, # causal
             sm_scale,
             q_data_type,
             kv_data_type,
         )
 
     def run(self, q_nope, q_pe, ckv, k_pe, return_lse = False):
-        return self.wrapper.run(q_nope, q_pe, ckv, k_pe, return_lse)
+        return self.wrapper.run(q_nope, q_pe, ckv, k_pe, return_lse = return_lse)
 
 class MLAWrapperSingleton():
     wrappers:dict = {}
@@ -179,6 +179,17 @@ class MLAWrapperSingleton():
                 sm_scale,
                 q_data_type,
                 kv_data_type,)
+            wrapper.need_plan = False
+            
+    @classmethod
+    def need_plan_all(cls):
+        for device, wrapper in cls.wrappers.items():
+            wrapper.need_plan = True
+        
+    @classmethod
+    def reset_buffer(cls):
+        for device, wrapper in cls.wrappers.items():
+            wrapper.qo_indptr_buf[1] = 1
             
 
 if __name__ == "__main__":
@@ -187,8 +198,9 @@ if __name__ == "__main__":
     page_size = 64
     num_heads = 128
 
-    q_nope = torch.randn((1, num_heads, 512), dtype=torch.bfloat16, device="cuda")
-    q_pe = torch.randn((1, num_heads, 64), dtype=torch.bfloat16, device="cuda")
+    q_len = 10
+    q_nope = torch.randn((q_len, num_heads, 512), dtype=torch.bfloat16, device="cuda")
+    q_pe = torch.randn((q_len, num_heads, 64), dtype=torch.bfloat16, device="cuda")
     ckv = torch.randn((max_pages, page_size, 512), dtype=torch.bfloat16, device="cuda")
     k_pe = torch.randn((max_pages, page_size, 64), dtype=torch.bfloat16, device="cuda")
     
@@ -199,10 +211,10 @@ if __name__ == "__main__":
         max_pages,
     )
     
-    kv_len_arr = torch.tensor([10], dtype=torch.int32, device="cuda")
-
+    kv_len_arr = torch.tensor([q_len], dtype=torch.int32, device="cuda")
+    qo_indptr = torch.tensor([0, q_len], dtype=torch.int32, device="cuda")
     wrapper.plan(
-        None,
+        qo_indptr,
         None,
         None,
         kv_len_arr,
@@ -216,6 +228,7 @@ if __name__ == "__main__":
     )
 
     attn_output = wrapper.run(q_nope, q_pe, ckv, k_pe)
+    print(attn_output.shape)
 
     k = (
         torch.cat([ckv, k_pe], dim=-1)
@@ -235,6 +248,7 @@ if __name__ == "__main__":
         False,
         192 ** (-0.5)
     )
+    print(attn_ref.shape)
     
     torch.testing.assert_close(attn_output, attn_ref, rtol=1e-3, atol=1e-3)
     print("test past")
\ No newline at end of file
diff --git a/ktransformers/operators/models.py b/ktransformers/operators/models.py
index 3877dbc..57d4bea 100644
--- a/ktransformers/operators/models.py
+++ b/ktransformers/operators/models.py
@@ -56,7 +56,7 @@ from ktransformers.models.modeling_deepseek import (
 from transformers.models.qwen2_moe.configuration_qwen2_moe import Qwen2MoeConfig
 from ktransformers.models.configuration_llama import LlamaConfig
 from ktransformers.operators.base_operator import BaseInjectedModule
-from ktransformers.util.utils import InferenceState
+from ktransformers.util.utils import InferenceState, get_compute_capability
 from ktransformers.util.custom_gguf import GGUFLoader
 from transformers.configuration_utils import PretrainedConfig
 from ktransformers.models.modeling_llama import (
@@ -649,7 +649,9 @@ class KDeepseekV2Model(BaseInjectedModule):
         if per_layer_prefill_flag:
             causal_mask = None
         else:
-            if os.name == 'nt':
+            if os.name == 'nt' or get_compute_capability()<8:
+                print("for Windows or GPU before ampere, use forward_windows")
+                # only use mask in forward windows or can't flash attn
                 causal_mask = self._update_causal_mask(
                     attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
                 )
diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml
index 6fb6586..d28e016 100644
--- a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml
+++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat.yaml
@@ -60,6 +60,7 @@
     kwargs:
       generate_device: "cuda"
       prefill_device: "cuda"
+      absorb_for_prefill: False # change this to True to enable long context(prefill may slower).
 - match:
     name: "^model$"
   replace:
diff --git a/ktransformers/server/backend/interfaces/ktransformers.py b/ktransformers/server/backend/interfaces/ktransformers.py
index 49a3f16..8e6e5f9 100644
--- a/ktransformers/server/backend/interfaces/ktransformers.py
+++ b/ktransformers/server/backend/interfaces/ktransformers.py
@@ -14,6 +14,7 @@ from ktransformers.models.custom_cache import StaticCache
 from ktransformers.util.cuda_graph_runner import CUDAGraphRunner
 from ktransformers.local_chat import custom_models, default_optimize_rules
 from ktransformers.util.utils import get_device
+from ktransformers.operators.flashinfer_wrapper import flashinfer_enabled, MLAWrapperSingleton
 
 
 warm_uped = False
@@ -186,6 +187,8 @@ class KTransformersInterface(TransformersInterface):
             input_ids = input_ids.to("cpu")
         inputs_embeds = self.model.model.embed_tokens(input_ids).to(device)
         torch.cuda.set_device(device)
+        if flashinfer_enabled:
+            MLAWrapperSingleton.need_plan_all()
         if self.use_static_cache:
             logits = self.model(
                 inputs_embeds=inputs_embeds,
@@ -198,6 +201,8 @@ class KTransformersInterface(TransformersInterface):
         else:
             logits = self.model(inputs_embeds=inputs_embeds, return_dict=False)[0]
 
+        if flashinfer_enabled:
+            MLAWrapperSingleton.reset_buffer()
         self.prepare_logits_wrapper(input_ids, device)
         next_token = self.logits_to_token(logits[0, -1, :])
         yield self.append_new_tokens(next_token)
diff --git a/ktransformers/server/backend/interfaces/transformers.py b/ktransformers/server/backend/interfaces/transformers.py
index 8211933..7e6bd15 100644
--- a/ktransformers/server/backend/interfaces/transformers.py
+++ b/ktransformers/server/backend/interfaces/transformers.py
@@ -333,7 +333,7 @@ class TransformersInterface(BackendInterfaceBase):
         for i in range(1, self.args.max_new_tokens):
             
             with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True):
-                if i > 1 and flashinfer_enabled:
+                if flashinfer_enabled:
                     MLAWrapperSingleton.plan_all(None,None,None,self.active_cache_position.to(torch.int32)+1,
                                              num_heads=self.model.config.num_attention_heads, head_dim_ckv=self.model.config.kv_lora_rank, 
                                              head_dim_kpe=self.model.config.qk_rope_head_dim, page_size=self.cache.page_size,
diff --git a/ktransformers/util/utils.py b/ktransformers/util/utils.py
index 5c608b1..1908373 100644
--- a/ktransformers/util/utils.py
+++ b/ktransformers/util/utils.py
@@ -21,6 +21,18 @@ from ktransformers.operators.flashinfer_wrapper import MLAWrapperSingleton
 
 warm_uped = False
 
+def get_compute_capability(device:torch.device = None):
+    if torch.cuda.is_available():
+        if device is None:
+            num_gpus = torch.cuda.device_count()
+            min_compute_capability_major = 100
+            for gpu_id in range(num_gpus):
+                gpu_props = torch.cuda.get_device_properties(gpu_id)
+                min_compute_capability_major = min(min_compute_capability_major, gpu_props.major)
+            return min_compute_capability_major
+        else:
+            return torch.cuda.get_device_properties(device)
+
 def set_module(model, submodule_key, module):
     tokens = submodule_key.split('.')
     sub_tokens = tokens[:-1]
@@ -153,6 +165,9 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
             inputs_embeds = model.model.embed_tokens(inputs.to("cpu"))
         else:
             inputs_embeds = model.model.embed_tokens(inputs.to("cpu")).to(torch_device)
+        if use_flashinfer_mla:
+            MLAWrapperSingleton.need_plan_all()
+            
         logits = model(
             inputs_embeds = inputs_embeds, cache_position=cache_position, past_key_values=past_key_values, return_dict=False, use_cache=True
         )[0][:,-1,:].unsqueeze(0).clone().to(torch_device)
@@ -175,6 +190,9 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
         else:
             next_token = torch.argmax(next_token_scores, dim=-1)
         first_token_time = time.time() - start_time
+        
+        if use_flashinfer_mla:
+            MLAWrapperSingleton.reset_buffer()
 
         prefill_count = seq_length
         prefill_time = first_token_time
@@ -192,15 +210,15 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
             
         start_time = time.time()
         for i in range(1, max_new_tokens):
+            if use_flashinfer_mla:
+                MLAWrapperSingleton.plan_all(None,None,None,position_ids.squeeze(1)+1,
+                                             num_heads, head_dim_ckv, head_dim_kpe, past_key_values.page_size,
+                                             q_head_dim ** (-0.5), torch.bfloat16, torch.bfloat16)
             global warm_uped
             if use_cuda_graph and ( (warm_uped == True and int(i) == 1) or (warm_uped == False and int(i) == 2) ):
                 warm_uped = True
                 cuda_graph_runner = CUDAGraphRunner()
                 cuda_graph_runner.capture(model, next_token.unsqueeze(0), position_ids, cache_position, past_key_values, torch_device, return_dict=False, use_cache=True)
-            if i > 1 and use_flashinfer_mla:
-                MLAWrapperSingleton.plan_all(None,None,None,position_ids.squeeze(1)+1,
-                                             num_heads, head_dim_ckv, head_dim_kpe, past_key_values.page_size,
-                                             q_head_dim ** (-0.5), torch.bfloat16, torch.bfloat16)
             next_token = decode_one_tokens(cuda_graph_runner, next_token.unsqueeze(0), position_ids, cache_position, past_key_values, use_cuda_graph).to(torch_device)
             inputs = torch.cat((inputs, next_token.unsqueeze(0)), dim=-1)
             generated_ids[:, cache_position] = next_token.int()

From 021822dd01b0ade6690ad358a46a4829de55ec84 Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Tue, 25 Feb 2025 09:02:32 +0000
Subject: [PATCH 07/14] update FAQ

---
 doc/en/FAQ.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/doc/en/FAQ.md b/doc/en/FAQ.md
index c8f58a2..3269f6b 100644
--- a/doc/en/FAQ.md
+++ b/doc/en/FAQ.md
@@ -72,3 +72,24 @@ The detailed error:
 Running `conda install -c conda-forge libstdcxx-ng` can solve the problem.
 
 
+### Q: When running the bfloat16 moe model, the data shows NaN
+The detailed error:
+```shell
+Traceback (most recent call last):
+  File "/root/ktransformers/ktransformers/local_chat.py", line 183, in <module>
+    fire.Fire(local_chat)
+  File "/usr/local/lib/python3.10/dist-packages/fire/core.py", line 135, in Fire
+    component_trace = _Fire(component, args, parsed_flag_args, context, name)
+  File "/usr/local/lib/python3.10/dist-packages/fire/core.py", line 468, in _Fire
+    component, remaining_args = _CallAndUpdateTrace(
+  File "/usr/local/lib/python3.10/dist-packages/fire/core.py", line 684, in _CallAndUpdateTrace
+    component = fn(*varargs, **kwargs)
+  File "/root/ktransformers/ktransformers/local_chat.py", line 177, in local_chat
+    generated = prefill_and_generate(
+  File "/root/ktransformers/ktransformers/util/utils.py", line 204, in prefill_and_generate
+    next_token = decode_one_tokens(cuda_graph_runner, next_token.unsqueeze(0), position_ids, cache_position, past_key_values, use_cuda_graph).to(torch_device)
+  File "/root/ktransformers/ktransformers/util/utils.py", line 128, in decode_one_tokens
+    next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
+RuntimeError: probability tensor contains either `inf`, `nan` or element < 0
+```
+**SOLUTION**: The issue of running ktransformers on Ubuntu 22.04 is caused by the current system's g++ version being too old, and the pre-defined macros do not include avx_bf16. We have tested and confirmed that it works on g++ 11.4 in Ubuntu 22.04.
\ No newline at end of file

From 0ca0b99fab7a98251ccb3eb9c51f8f877a4d2bc4 Mon Sep 17 00:00:00 2001
From: liam <yzwliam@126.com>
Date: Tue, 25 Feb 2025 17:19:19 +0800
Subject: [PATCH 08/14] :zap: update git ignore add docker dev container

---
 .devcontainer/Dockerfile             | 19 ++++++++++++++++
 .devcontainer/devcontainer.json      | 34 ++++++++++++++++++++++++++++
 .gitignore                           |  8 ++-----
 ktransformers/tests/mmlu_pro_test.py |  4 ++--
 4 files changed, 57 insertions(+), 8 deletions(-)
 create mode 100644 .devcontainer/Dockerfile
 create mode 100644 .devcontainer/devcontainer.json

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
new file mode 100644
index 0000000..5c2606f
--- /dev/null
+++ b/.devcontainer/Dockerfile
@@ -0,0 +1,19 @@
+FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel as compile_server
+WORKDIR /workspace
+ENV CUDA_HOME /usr/local/cuda
+RUN <<EOF
+apt update -y &&  apt install -y  --no-install-recommends \
+    git \
+    wget \
+    vim \
+    gcc \
+    g++ \
+    cmake && 
+rm -rf /var/lib/apt/lists/* &&
+cd ktransformers &&
+pip install ninja pyproject numpy cpufeature &&
+pip install flash-attn &&
+cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /opt/conda/lib/
+EOF
+# Set the default shell to bash
+CMD ["/bin/bash"]
\ No newline at end of file
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000..41007ac
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,34 @@
+{
+    "name": "Ktrans Dev Container",
+    "privileged": true,
+    "build": {
+        "dockerfile": "Dockerfile",
+        "context": "..",
+        "args": {
+            "http_proxy": "${env:http_proxy}",
+            "https_proxy": "${env:https_proxy}",
+        }
+    },
+    "runArgs": [
+        "--network=host",
+        "--gpus",
+        "all"
+        // "--gpu all"
+    ],
+    "workspaceFolder": "/workspace",
+    "workspaceMount": "source=${localWorkspaceFolder},target=/workspace,type=bind,consistency=cached",
+    "mounts": [
+        "source=/mnt/data,target=/mnt/incontainer,type=bind,consistency=cached"
+    ],
+    "customizations": {
+        "vscode": {
+            "extensions": [
+            ],
+            "settings": {
+                "terminal.integrated.shell.linux": "/bin/bash",
+                "cmake.configureOnOpen": true,
+                "cmake.generator": "Ninja"
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 58250d1..7e186ef 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,13 +19,9 @@ ktransformers/server/local_store/
 ktransformers/server_test1.db
 *.patch
 img/
-tmp1.txt
-test_65_300_1536.txt
+tmp*.txt
 test.txt
 book
-ktransformers/tests/mmlu_result_silicon.json
 ktransformers/tests/chat_txt.txt
-mmlu_result_q4km.json
-mmlu_result_q4km.log
-ktransformers/tests/mmlu_result_silicon.log
+mmlu_result*
 ktransformers/ktransformers_ext/cuda_musa/
diff --git a/ktransformers/tests/mmlu_pro_test.py b/ktransformers/tests/mmlu_pro_test.py
index 27eb9b2..dffe296 100644
--- a/ktransformers/tests/mmlu_pro_test.py
+++ b/ktransformers/tests/mmlu_pro_test.py
@@ -173,8 +173,8 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="API Generate Tester")
     parser.add_argument("--concurrent", type=int, default=1000, help="Number of concurrent evaluations")
     parser.add_argument("--file", type=str, default="TIGER-Lab/MMLU-Pro", help="Path to the mmlu.jsonl file")
-    parser.add_argument("--result", type=str, default="./mmlu_pro.json", help="Path to save the result JSON file")
-    parser.add_argument("--log", type=str, default="./mmlu_pro.log", help="Path to save the log file")
+    parser.add_argument("--result", type=str, default="./mmlu_result_pro.json", help="Path to save the result JSON file")
+    parser.add_argument("--log", type=str, default="./mmlu_result_pro.log", help="Path to save the log file")
     parser.add_argument("--model", type=str, default="Pro/deepseek-ai/DeepSeek-V3", help="Model name or path")
     parser.add_argument("--api_url", type=str, default="http://localhost:15488/v1/chat/completions", help="API URL")
     # parser.add_argument("--api_url", type=str, default="https://api.siliconflow.cn/v1/chat/completions", help="API URL")

From 1b5ac67fcaa3760e6d96244e7ccacfe2c5737976 Mon Sep 17 00:00:00 2001
From: liam <yzwliam@126.com>
Date: Tue, 25 Feb 2025 17:45:17 +0800
Subject: [PATCH 09/14] :memo: add benchmark.md

---
 doc/en/benchmark.md | 43 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 doc/en/benchmark.md

diff --git a/doc/en/benchmark.md b/doc/en/benchmark.md
new file mode 100644
index 0000000..a07f240
--- /dev/null
+++ b/doc/en/benchmark.md
@@ -0,0 +1,43 @@
+## Benchmark
+
+To conduct a quick and convenient check, we have employed a simple Python script available [here](https://github.com/kvcache-ai/ktransformers/tree/main/ktransformers/tests) to assess the precision of our **[ktransformers](https://github.com/kvcache-ai/ktransformers)** project. For this evaluation, we utilized the same dataset, which was shuffled in a consistent manner and limited to the first 1,000 data points, to test our implementation across a variety of CPU kernels, MLA kernels, and quantization formats.
+
+We selected the DeepSeek-V3 model in its bf16, int8, and q4km versions for this test. The MMLU dataset, which can be found [here](https://huggingface.co/datasets/cais/mmlu), was used (we selected all datasets and shuffled them with a fixed random seed).
+
+**!!! However, we skipped the few-shot part and only chose the first 1,000 data points for a quick check.** Please note that this approach may result in results that are not consistent with the technical report of DeepSeek-V3. And the test of R1 and further more tests are on going.
+
+To verify our results, we chose [cloud service platform](https://cloud.siliconflow.cn/models) as baseline. All tests were conducted using the same script and datasets, allowing us to make a preliminary assessment of our project's precision.
+
+We set the argument `temperature=0.6`, and to simplify the test process, we skipped the few-shot part and used the following prompt: `There is a single choice question. Answer the question by replying A, B, C, D. No other answers are accepted. Just the letter. \nQuestion: {question}\nA. {option_a}\nB. {option_b}\nC. {option_c}\nD. {option_d}\nAnswer: '`. For more details, please refer to the [script](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/tests/mmlu_test.py).
+
+Given that we have only tested 1,000 cases, which provides only a preliminary judgment, some fluctuations in the results are reasonable. We selected all datasets and shuffled them with a fixed random seed to ensure consistency.
+
+## Some Detail
+
+- The bf16 model of DeepSeek-V3 is available [here](https://huggingface.co/opensourcerelease/DeepSeek-V3-bf16/tree/main) (you may convert it to gguf by llama.cpp). The q4km model can be found [here](https://huggingface.co/unsloth/DeepSeek-V3-GGUF/tree/main/DeepSeek-V3-Q4_K_M).
+    
+- The optimization YAML file is located [here](https://github.com/kvcache-ai/ktransformers/tree/main/ktransformers/optimize/optimize_rules). For the Matrix MUL Kernel, you can change `KLinearMarlin` to `KLinearTorch`.
+    
+- To switch the MLA Kernel from Triton to Torch, you can check and modify [this file](https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/operators/attention.py), specifically by using the `forward_windows` method.
+    
+- When attempting to conduct the bf16 test (both CPU Weight and GPU Weight), you may encounter issues stemming from older versions of g++ and as, particularly when using Ubuntu 20 or earlier versions. To facilitate a smoother experience and enable you to reproduce our results, we have provided a development container. This container offers a pre-configured environment tailored for this purpose. However, please note that the container does not have the ktrans package installed. Therefore, you may still need to manually install certain packages to ensure everything runs smoothly.
+    
+    - You may config the model mount dir in `devcontainer/devcontainer.json`, check the `"mouts":` config.
+
+
+## The Result Table
+
+|                          |                   |            |                   |         |            |                                                        |              |
+| ------------------------ | ----------------- | ---------- | ----------------- | ------- | ---------- | ------------------------------------------------------ | ------------ |
+| DataSet                  | CPU Weight Format | CPU Kernel | GPU Weight Format | GEMM    | MLA Kernel | [Siliconflow](https://cloud.siliconflow.cn/models)<br> | Ktrans Point |
+| MMLU<br><br>(shuffle 1k) | bf16              | cpuinfer   | bf16              | torch   | torch      | 81.6                                                   | 81.9         |
+|                          | int8              | cpuinfer   | bf16              | torch   | torch      | 81.6                                                   | 83.1         |
+|                          | q4km              | cpuinfer   | bf16              | torch   | torch      | 81.6                                                   | 82.8         |
+|                          | q4km              | cpuinfer   | bf16              | torch   | triton     | 81.6                                                   | 81.4         |
+|                          | q4km              | cpuinfer   | q4km->marlin 8    | marlin  | triton     | 81.6                                                   | 81.1         |
+|                          | q4km              | cpuinfer   | q4km->marlin 4    | marlin  | triton     | 81.6                                                   | 81           |
+|                          | q4km              | cpuinfer   | fp8               | marlin  | triton     | 81.6                                                   | 81.5         |
+| MMLU-pro                 | q4km              | cpuinfer   | fp8               | fp8gemm | triton     | 57.7                                                   | 57.6         |
+| MMLU-pro                 | q4km              | cpuinfer   | q4km->marlin 4    | marlin  | triton     | 57.7                                                   | 57.5         |
+| HumanEval                | tbd               | tbd        | tbd               | tbd     | tbd        | tbd                                                    | tbd          |
+| GSM8K                    | tbd               | tbd        | tbd               | tbd     | tbd        | tbd                                                    | tbd          |

From 7e5962af3d16af570f3108acd3238d9dae330a45 Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Tue, 25 Feb 2025 10:52:29 +0000
Subject: [PATCH 10/14] fix fp8 multi gpu; update FQA

---
 doc/en/FAQ.md                                     | 6 +++++-
 ktransformers/ktransformers_ext/triton/fp8gemm.py | 3 ++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/doc/en/FAQ.md b/doc/en/FAQ.md
index 3269f6b..a8399bd 100644
--- a/doc/en/FAQ.md
+++ b/doc/en/FAQ.md
@@ -92,4 +92,8 @@ Traceback (most recent call last):
     next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
 RuntimeError: probability tensor contains either `inf`, `nan` or element < 0
 ```
-**SOLUTION**: The issue of running ktransformers on Ubuntu 22.04 is caused by the current system's g++ version being too old, and the pre-defined macros do not include avx_bf16. We have tested and confirmed that it works on g++ 11.4 in Ubuntu 22.04.
\ No newline at end of file
+**SOLUTION**: The issue of running ktransformers on Ubuntu 22.04 is caused by the current system's g++ version being too old, and the pre-defined macros do not include avx_bf16. We have tested and confirmed that it works on g++ 11.4 in Ubuntu 22.04.
+
+### Q: Using fp8 prefill very slow.
+
+The FP8 kernel is build by JIT, so the first run will be slow. The subsequent runs will be faster.
\ No newline at end of file
diff --git a/ktransformers/ktransformers_ext/triton/fp8gemm.py b/ktransformers/ktransformers_ext/triton/fp8gemm.py
index 7d5b72e..d5c913d 100644
--- a/ktransformers/ktransformers_ext/triton/fp8gemm.py
+++ b/ktransformers/ktransformers_ext/triton/fp8gemm.py
@@ -102,7 +102,8 @@ def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> t
     M, N = x.size()
     y = torch.empty_like(x, dtype=torch.get_default_dtype())
     grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']), triton.cdiv(N, meta['BLOCK_SIZE']))
-    weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
+    with torch.cuda.device(x.device):
+        weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
     return y
 
 

From 477ac28a9c5002a066a956320297d2ec1c098d52 Mon Sep 17 00:00:00 2001
From: Atream <boxin-zhang@qq.com>
Date: Tue, 25 Feb 2025 12:47:31 +0000
Subject: [PATCH 11/14] fix-update-flashinfer_wrapper_local_chat

---
 ktransformers/operators/attention.py                  |  3 +--
 ktransformers/operators/flashinfer_wrapper.py         | 11 +++++++++--
 .../optimize_rules/DeepSeek-V3-Chat-multi-gpu-4.yaml  |  4 ++++
 ktransformers/util/utils.py                           |  1 +
 4 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/ktransformers/operators/attention.py b/ktransformers/operators/attention.py
index 5e7391f..35c8093 100644
--- a/ktransformers/operators/attention.py
+++ b/ktransformers/operators/attention.py
@@ -435,7 +435,6 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                                         q_nope.dtype,
                                         compressed_kv.dtype)
             attn_output = self.mla_wrapper.run(q_nope, q_pe, compressed_kv, k_pe).view(bsz, q_len, self.num_heads, self.kv_lora_rank)
-
             """
             k = (
                 torch.cat([compressed_kv, k_pe], dim=-1)
@@ -465,7 +464,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
             
             attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim) # [bsz, q_len, self.num_heads * self.v_head_dim]
             attn_output = self.o_proj(attn_output)
-
+            
             return attn_output, None, past_key_value
         else:
             if past_key_value is not None:
diff --git a/ktransformers/operators/flashinfer_wrapper.py b/ktransformers/operators/flashinfer_wrapper.py
index 864b33e..f8ea3ce 100644
--- a/ktransformers/operators/flashinfer_wrapper.py
+++ b/ktransformers/operators/flashinfer_wrapper.py
@@ -122,7 +122,7 @@ class MLAWrapper():
         if kv_indices is None:
             assert self.max_batch_size == 1
             kv_indices = self.kv_indices_buf
-
+        
         self.wrapper.plan(
             qo_indptr,
             kv_indptr,
@@ -189,7 +189,14 @@ class MLAWrapperSingleton():
     @classmethod
     def reset_buffer(cls):
         for device, wrapper in cls.wrappers.items():
-            wrapper.qo_indptr_buf[1] = 1
+            wrapper.qo_indptr_buf[1] = 1 # assert max_batch_size=1 here.
+            
+    @classmethod
+    def update_buffer(cls, max_pages):
+        for device, wrapper in cls.wrappers.items():
+            wrapper.kv_indptr_buf[1] = max_pages # assert max_batch_size=1 here.
+            wrapper.kv_indices_buf = torch.arange(0, max_pages, dtype=torch.int32, device=device)
+            wrapper.wrapper._kv_indices_buf = wrapper.kv_indices_buf
             
 
 if __name__ == "__main__":
diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-4.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-4.yaml
index 03c85a0..ea75b30 100644
--- a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-4.yaml
+++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-4.yaml
@@ -293,6 +293,7 @@
     kwargs:
       generate_device: "cuda:0"
       prefill_device: "cuda:0"
+      absorb_for_prefill: False
 
 # GPU 1: layers 15–29
 - match:
@@ -302,6 +303,7 @@
     kwargs:
       generate_device: "cuda:1"
       prefill_device: "cuda:1"
+      absorb_for_prefill: False
 
 # GPU 2: layers 30–44
 - match:
@@ -311,6 +313,7 @@
     kwargs:
       generate_device: "cuda:2"
       prefill_device: "cuda:2"
+      absorb_for_prefill: False
 
 # GPU 3: layers 45–60
 - match:
@@ -320,6 +323,7 @@
     kwargs:
       generate_device: "cuda:3"
       prefill_device: "cuda:3"
+      absorb_for_prefill: False
 
 # === Overall Model Replacement with Transfer Map ===
 
diff --git a/ktransformers/util/utils.py b/ktransformers/util/utils.py
index 64b9131..3f5ad8e 100644
--- a/ktransformers/util/utils.py
+++ b/ktransformers/util/utils.py
@@ -177,6 +177,7 @@ def prefill_and_generate(model, tokenizer, inputs, max_new_tokens=10000, use_cud
         else:
             inputs_embeds = model.model.embed_tokens(inputs.to("cpu")).to(torch_device)
         if use_flashinfer_mla:
+            MLAWrapperSingleton.update_buffer(past_key_values.max_pages)
             MLAWrapperSingleton.need_plan_all()
             
         logits = model(

From 2c0cce90d0d2bf9e361e514134fa5689f9f46db4 Mon Sep 17 00:00:00 2001
From: Azure <azuretang@foxmail.com>
Date: Tue, 25 Feb 2025 13:32:09 +0000
Subject: [PATCH 12/14] add fp8 multi gpu yaml example

---
 ...hat-multi-gpu-fp8-linear-ggml-experts.yaml | 157 ++++++++++++++++++
 1 file changed, 157 insertions(+)
 create mode 100644 ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-fp8-linear-ggml-experts.yaml

diff --git a/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-fp8-linear-ggml-experts.yaml b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-fp8-linear-ggml-experts.yaml
new file mode 100644
index 0000000..fa8c03d
--- /dev/null
+++ b/ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-multi-gpu-fp8-linear-ggml-experts.yaml
@@ -0,0 +1,157 @@
+- match:
+    name: "^model.embed_tokens"
+  replace:
+    class: "default"
+    kwargs:
+        generate_device: "cpu"
+        prefill_device: "cpu"
+
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\.([3456][0-9])\\."
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3RotaryEmbedding
+  replace:
+    class: ktransformers.operators.RoPE.YarnRotaryEmbeddingV3
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.(?!self_attn\\.kv_b_proj).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+      generate_op: "KLinearFP8"
+      prefill_op: "KLinearTorch"
+
+- match:
+    name: "^model\\.layers\\.([3456][0-9])\\.(?!self_attn\\.kv_b_proj).*$"  # regular expression 
+    class: torch.nn.Linear  # only match modules matching name and class simultaneously
+  replace:
+    class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+      generate_op: "KLinearFP8"
+      prefill_op: "KLinearTorch"
+  
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\.([3456][0-9])\\.mlp$"
+    class: ktransformers.models.modeling_deepseek_v3.DeepseekV3MoE
+  replace:
+    class: ktransformers.operators.experts.KDeepseekV3MoE     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.gate$"
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+- match:
+    name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.gate$"
+    class: ktransformers.models.modeling_deepseek_v3.MoEGate
+  replace:
+    class: ktransformers.operators.gate.KMoEGate     # mlp module with custom forward function
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda:0"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op:  "KExpertsCPU"
+      out_device: "cuda:0"
+  recursive: False # don't recursively inject submodules of this module
+
+- match:
+    name: "^model\\.layers\\.([3456][0-9])\\.mlp\\.experts$"
+  replace:
+    class: ktransformers.operators.experts.KTransformersExperts     # custom MoE Kernel with expert paralleism
+    kwargs:
+      prefill_device: "cuda:1"
+      prefill_op: "KExpertsTorch"
+      generate_device: "cpu"
+      generate_op:  "KExpertsCPU"
+      out_device: "cuda:1"
+  recursive: False # don't recursively inject submodules of this module
+
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+      absorb_for_prefill: False # change this to True to enable long context(prefill may slower).
+
+- match:
+    name: "^model\\.layers\\.([3456][0-9])\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+      absorb_for_prefill: False # change this to True to enable long context(prefill may slower).
+
+- match:
+    name: "^model$"
+  replace:
+    class: "ktransformers.operators.models.KDeepseekV2Model"
+    kwargs:
+      per_layer_prefill_intput_threshold: 0 # 0 is close layer wise prefill
+      transfer_map: 
+        30: "cuda:1"
+
+- match:
+    name: "^model\\.layers\\.(0|[1-9]|[12][0-9])\\."
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cuda:0"
+      prefill_device: "cuda:0"
+
+- match:
+    name: "^lm_head"
+    class: torch.nn.Linear
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"
+
+
+- match:
+    name: "(^model\\.layers\\.([3456][0-9])\\.)|(model.norm)"
+  replace:
+    class: "default"
+    kwargs:
+      generate_device: "cuda:1"
+      prefill_device: "cuda:1"

From 03f8bc9f79d9b3915bce73ab13174f91e53a79d9 Mon Sep 17 00:00:00 2001
From: Atream <80757050+Atream@users.noreply.github.com>
Date: Tue, 25 Feb 2025 21:35:31 +0800
Subject: [PATCH 13/14] Update DeepseekR1_V3_tutorial.md add long context

---
 doc/en/DeepseekR1_V3_tutorial.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/doc/en/DeepseekR1_V3_tutorial.md b/doc/en/DeepseekR1_V3_tutorial.md
index 52e9d32..22cfab7 100644
--- a/doc/en/DeepseekR1_V3_tutorial.md
+++ b/doc/en/DeepseekR1_V3_tutorial.md
@@ -154,6 +154,18 @@ the output quality doesn't change. But the speed of decoding and prefill
 is speed up which is inspiring. So our showcase makes use of this finding*
 
 ## How to Run
+### V0.2.2 longer context
+If you want to use long context(longer than 20K) for prefill, enable the matrix absorption MLA during the prefill phase, which will significantly reduce the size of the kv cache. Modify yaml file like this:
+```
+- match:
+    name: "^model\\.layers\\..*\\.self_attn$"
+  replace:
+    class: ktransformers.operators.attention.KDeepseekV2Attention # optimized MLA implementation
+    kwargs:
+      generate_device: "cuda"
+      prefill_device: "cuda"
+      absorb_for_prefill: True # change this to True to enable long context(prefill may slower).
+```
 ### V0.2 & V0.2.1 Showcase
 #### Single socket version (32 cores)
 Our local_chat test command is:

From 13974eb2642156118ae16ba469f7ddc9265b0498 Mon Sep 17 00:00:00 2001
From: Atream <80757050+Atream@users.noreply.github.com>
Date: Tue, 25 Feb 2025 21:36:52 +0800
Subject: [PATCH 14/14] Update DeepseekR1_V3_tutorial.md

---
 doc/en/DeepseekR1_V3_tutorial.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/en/DeepseekR1_V3_tutorial.md b/doc/en/DeepseekR1_V3_tutorial.md
index 22cfab7..84d3418 100644
--- a/doc/en/DeepseekR1_V3_tutorial.md
+++ b/doc/en/DeepseekR1_V3_tutorial.md
@@ -16,6 +16,7 @@
 			- [Memory consumptions:](#memory-consumptions)
 			- [Benchmark results](#benchmark-results-2)
 	- [How to Run](#how-to-run)
+ 		- [V0.2.2 longer context](#v022-longer-context)
 		- [V0.2 \& V0.2.1 Showcase](#v02--v021-showcase)
 			- [Single socket version (32 cores)](#single-socket-version-32-cores)
 			- [Dual socket version (64 cores)](#dual-socket-version-64-cores)