{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "e5aab976",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "# Extend Thunder with CUDA-Python\n",
    "\n",
    "In this demo, we implement a (naive, unoptimized) version of the flash attention 2 algorithm from \n",
    "[Tri Dao: FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://tridao.me/publications/flash2/flash2.pdf), following this pseudocode which we took from the paper.\n",
    "\n",
    "Our implementation won't be quite as fast as flash attention, but we will learn how to use a CUDA kernel from Python/PyTorch/Thunder by extending Thunder with NVIDIA's [CUDA-Python](https://github.com/NVIDIA/cuda-python) low-level bindings, and then you can do it for your own, even more awesome kernels.\n",
    "\n",
    "There is not much special about the Thunder part of this, so if you looked at the extending Thunder section in the [Zero to Thunder tutorial](./zero_to_thunder.ipynb) things should look very familiar, but as CUDA-Python is relatively new, we thought it might be neat to have a spelled-out example here.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "OK, Now we know what to do. Let's import some modules and get a few sample inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718c1f3e",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "import torch, math, itertools, numpy\n",
    "\n",
    "N_inp = 512\n",
    "N_out = 512\n",
    "d = 128\n",
    "\n",
    "with torch.device(\"cuda\"):\n",
    "    Q = torch.randn(96, N_out, d)\n",
    "    K = torch.randn(96, N_inp, d)\n",
    "    V = torch.randn(96, N_inp, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d4e193",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "The first thing we do is implement a quite literal translation of the pseudo code into a Python function using tensors for the tiles.\n",
    "We are not terribly ambitious here and assume that `B_c`and `B_r` divide N_inp and N_out, that N_inp and N_out are actually the same etc.\n",
    "\n",
    "You might improve the generality (and we welcome your PR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "158809ac",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def flash_attention_reference(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor, is_causal: bool = False, scale: float | None =None):\n",
    "    # N.B.: This uses the PyTorch SDPA tensor shape of batch, head_no, seq_len, head_dim\n",
    " \n",
    "    *batch, N_inp, d = K.shape\n",
    "    *_, N_out, _ = Q.shape\n",
    "\n",
    "    # assert shape compat\n",
    "    \n",
    "    \n",
    "    O = V.new_zeros(*batch, N_out, d)\n",
    "    L = V.new_zeros(*batch, N_out, 1)\n",
    "\n",
    "    dtype = O.dtype\n",
    "    device = O.device\n",
    "\n",
    "    neginf = torch.tensor(-math.inf, dtype=Q.dtype, device=Q.device)\n",
    "\n",
    "    B_c = 16  # this is NOT what the original impl uses\n",
    "    B_r = 16\n",
    "    T_c = (N_inp + B_c - 1) // B_c\n",
    "    T_r = (N_out + B_r - 1) // B_r\n",
    "\n",
    "    if scale is None:\n",
    "        scale = 1 / math.sqrt(d)\n",
    "\n",
    "    for block in itertools.product(*(range(s) for s in batch)):\n",
    "        # Q and O L split into T_r; K, V in T_c blocks\n",
    "        for i in range(T_r):\n",
    "            Q_i = Q[block][i * B_r: (i+1) * B_r]\n",
    "            O_i = torch.zeros(B_r, d, device=device, dtype=dtype)\n",
    "            l_i = torch.zeros(B_r, 1, device=device, dtype=dtype)\n",
    "            m_i = torch.full((B_r, 1), -math.inf, device=device, dtype=dtype)\n",
    "            last_m_i = m_i\n",
    "            for j in range(T_c):\n",
    "                if is_causal and j * B_c > (i+1) * B_r - 1:\n",
    "                    break\n",
    "                # in Python 3.11+ we could write K[*block, j * B_c: (j + 1) * B_c] instead...\n",
    "                K_j = K[block][j * B_c: (j + 1) * B_c]\n",
    "                V_j = V[block][j * B_c: (j + 1) * B_c]\n",
    "                S_i = scale * (Q_i @ K_j.T)\n",
    "                if is_causal and i * B_r < (j + 1) * B_c - 1:\n",
    "                    mask = torch.arange(i*B_r, (i+1)*B_r, device=device, dtype=dtype)[:, None] >= torch.arange(j*B_c, (j+1) * B_c, device=device, dtype=dtype)[None, :]\n",
    "                    S_i = torch.where(mask, S_i, neginf)\n",
    "\n",
    "                m_i = torch.maximum(m_i, S_i.max(dim=-1, keepdim=True).values)\n",
    "                P_i = torch.exp(S_i - m_i)\n",
    "                l_i = torch.exp(last_m_i - m_i) * l_i + P_i.sum(dim=-1, keepdim=True)\n",
    "                O_i = torch.exp(last_m_i - m_i) * O_i + P_i @ V_j\n",
    "                last_m_i = m_i\n",
    "            O_i = (1.0 / l_i) * O_i\n",
    "            L_i = m_i + torch.log(l_i)\n",
    "            O[block][i * B_r: (i + 1) * B_r] = O_i\n",
    "            L[block][i * B_r: (i + 1) * B_r] = L_i\n",
    "    return O, L\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ae13ec",
   "metadata": {},
   "source": [
    "Let's see if our function computes the same thing as the PyTorch `scaled_dot_product_attention`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab9ee99e",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "actual, _ = flash_attention_reference(Q, K, V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4648ba0",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "expected = torch.nn.functional.scaled_dot_product_attention(Q, K, V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0f8ea7f",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.1921e-06, device='cuda:0')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(actual - expected).abs().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d3b173",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "That is neat! But we wanted to write our own CUDA kernel, so let us get out CUDA-Python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31cc1ead-9039-42e2-945b-4986161d42fd",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "## Using CUDA-Python to compile CUDA kernels for PyTorch\n",
    "\n",
    "[CUDA-Python](https://github.com/NVIDIA/cuda-python) provides low-level bindings to the CUDA and NVRTC (NVIDIA Run Time Compiler) API. We install it with `pip install cuda-python`.\n",
    "\n",
    "As those functions are very (I mean extremely) low level, we provide here a couple of helper functions for the following:\n",
    "\n",
    "- The function `compile_program_and_get_kernel` takes source code and produces a CUDA kernel from it,\n",
    "- The function `launch_kernel` runs one of our kernels with the specified arguments.\n",
    "\n",
    "We first import the `cuda` and `nvrtc` modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "471a1133",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "from cuda.bindings import driver, nvrtc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a37fe6",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "The function `compile_program_and_get_kernel` compiles the source code through NVRTC obtaining a PTX. This\n",
    "is then loaded (and compiled to SASS by `cuModuleLoadData`).\n",
    "Quite likely, one would want to let users access some of the other bits (e.g. compile flags), but we have to stop somewhere...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "017cd520",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def compile_program_and_get_kernel(cuda_src, function_name):\n",
    "    \"\"\"\n",
    "    Compiles a kernel from the CUDA source code provided in the string `cuda_src` and get the kernel with the name `function_name` (which needs to be\n",
    "    defined as extern \"C\" in the CUDA source code).\n",
    "    \n",
    "    \n",
    "    The kernel can then be launched with `launch_kernel`\n",
    "    \"\"\"\n",
    "\n",
    "    def check_error(results):\n",
    "        err, *results = results\n",
    "        if isinstance(err, driver.CUresult):\n",
    "            if err != driver.CUresult.CUDA_SUCCESS:\n",
    "                _, err_str = driver.cuGetErrorString(err)\n",
    "                raise RuntimeError(f\"CUDA error: {err_str.decode()}\")\n",
    "        elif isinstance(err, nvrtc.nvrtcResult):\n",
    "            if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:\n",
    "                logSize = check_error(nvrtc.nvrtcGetProgramLogSize(prog))\n",
    "                log = b\" \" * logSize\n",
    "                check_error(nvrtc.nvrtcGetProgramLog(prog, log))\n",
    "                print(log.decode())\n",
    "                _, err_str = nvrtc.nvrtcGetErrorString(err)\n",
    "                raise RuntimeError(f\"NVRTC error: {err_str.decode()}\")\n",
    "        else:\n",
    "            raise TypeError(\"Unknown error type: {err}\")\n",
    "        if len(results) == 0:\n",
    "            return\n",
    "        if len(results) == 1:\n",
    "            return results[0]\n",
    "        return results\n",
    "\n",
    "    torch.cuda.current_stream()  # this initializes the device context for us. we don't need the stream specifically.\n",
    "    \n",
    "    # Create program\n",
    "    prog = check_error(nvrtc.nvrtcCreateProgram(str.encode(cuda_src), (function_name + '.cu').encode(), 0, [], []))    \n",
    "    \n",
    "    # Compile program\n",
    "    min, maj = torch.cuda.get_device_capability()\n",
    "    opts = [f\"--gpu-architecture=compute_{min}{maj}\".encode()] #, b\"--expt-relaxed-constexpr\"]\n",
    "    check_error(nvrtc.nvrtcCompileProgram(prog, len(opts), opts))\n",
    "    \n",
    "    ## Get PTX from compilation\n",
    "    ptxSize = check_error(nvrtc.nvrtcGetPTXSize(prog))\n",
    "    ptx = b\" \" * ptxSize\n",
    "    check_error(nvrtc.nvrtcGetPTX(prog, ptx))\n",
    "\n",
    "    # Load PTX as module data and retrieve function\n",
    "    module = check_error(driver.cuModuleLoadData(ptx))\n",
    "    kernel = check_error(driver.cuModuleGetFunction(module, function_name.encode()))\n",
    "    return kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa797c6",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "Phew. If you're into CUDA programming (I guess you are, but you might check out the [CUDA-MODE](https://github.com/cuda-mode/resource-stream) series if you want to learn more. In fact, this notebooks started as a demo for a [lecture there](https://www.youtube.com/watch?v=zEuwuCTEf_0)), you know that to launch you need to specify block (the number of threads as a 3d \"array\") and grid (the number of blocks, again as a 3d \"array\") layout as well as dynamic shared memory.\n",
    "\n",
    "Another important detail is how we need to pass kernel arguments to `cuLaunchKernel`: We need to set up an array of pointers with the pointers pointing to a (CPU) memory address that contains the parameter (which, in the case of tensors, is a pointer itself). To facilitate having type information, we use numpy scalar types (e.g. `numpy.float32(0.5)`) for the arguments. Note that we don't check whether the kernel actually takes the parameters you give it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4628c9c2",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def launch_kernel(kernel, grid, block, /, *args, shmem=0):\n",
    "    \"\"\"utility function to launch kernels.\n",
    "    Args can be tensors (corresponding to float* etc kernel params or numpy scalars (which have precision info))\n",
    "    \"\"\"\n",
    "    \n",
    "    # collect values (data_ptr as uint64 array for tensors, the values as an array for values)\n",
    "    addresses = []\n",
    "    wrapped_args = []\n",
    "    for a in args:\n",
    "        if isinstance(a, torch.Tensor):\n",
    "            # for tensor pass in data_ptr\n",
    "            wrapped_args.append(numpy.array(a.data_ptr(), dtype=numpy.uint64))\n",
    "        elif isinstance(a, numpy.number):\n",
    "            wrapped_args.append(numpy.array([a]))\n",
    "        else:\n",
    "            raise TypeError(\"please only pass tensors and numpy numbers to launch_kernel\")\n",
    "            \n",
    "    # assemble an array of pointers to the args\n",
    "    args = numpy.array([a.ctypes.data for a in wrapped_args], dtype=numpy.uint64)\n",
    "\n",
    "    # set up grid / block layout to be 3d\n",
    "    grid = tuple(grid)\n",
    "    block = tuple(block)\n",
    "    assert 1 <= len(block) <= 3 and 1 <= len(grid) <= 3\n",
    "    grid = grid + (3 - len(grid)) * (1,)\n",
    "    block = block + (3 - len(block)) * (1,)\n",
    "    \n",
    "    # Launch!\n",
    "    err, = driver.cuLaunchKernel(\n",
    "       kernel,\n",
    "       *grid, *block, # xyz each\n",
    "       shmem,  # dynamic shared memory\n",
    "       torch.cuda.current_stream().stream_id,  # stream\n",
    "       args.ctypes.data,  # kernel arguments\n",
    "       0,  # extra (ignore)\n",
    "    )\n",
    "    if err != driver.CUresult.CUDA_SUCCESS:\n",
    "        raise RuntimeError(f\"CUDA error: {err}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aace14d",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "## A native flash attention kernel\n",
    "\n",
    "With these two done, we can implement our flash attention kernel. As with matmul, the tiling is important. But given head size can be large (128 for LLama-2 7B), using tiles that are large also in the other dimensions puts quite a strain on our on-chip memory resources (we should move to 16 bit floats, really - will you send a PR?).\n",
    "We put tiles for `Q`, `K`, `V` and `S` (a tile of the matrix of the attention weights or intermediate results for it) in shared memory and `l` and `m` (the denominator of the softmax split as factor and maximum of the log for the stabilization) and `O` tiles into the registers. The other bits are more or less a spelled-out version of the Python version, with a bit more nuisance to implement array operations by spreading across threads and/or loops.\n",
    "\n",
    "Again, we have many gaps (for shapes that do not divide tile sizes etc. and limitations (no causal yet)), but maybe you find the general idea helpful.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73570568",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<CUfunction 0x142944e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cuda_d = 128\n",
    "cuda_B_r = 32\n",
    "cuda_B_c = 16\n",
    "\n",
    "\n",
    "cuda_src = (\n",
    "f\"\"\"\n",
    "constexpr int B_r = {cuda_B_r};\n",
    "constexpr int B_c = {cuda_B_c};\n",
    "constexpr int d = {cuda_d};\n",
    "constexpr int o_per_thread_x = 1;\n",
    "constexpr int o_per_thread_y = 128/32;\n",
    "\"\"\"\n",
    "    + r\"\"\"\\\n",
    "#define NEG_INFINITY __int_as_float(0xff800000)\n",
    "\n",
    "extern \"C\" __global__\n",
    "void silly_attn(float *out, float* out_l, float *K, float *Q, float* V, float scaling, int batch_stride, int T_r, int T_c)\n",
    "{\n",
    "  int tid_x = threadIdx.x;\n",
    "  int tid_y = threadIdx.y;\n",
    "  int batch_offset = batch_stride * blockIdx.x;\n",
    "\n",
    "  __shared__ float Q_i[B_r][d];\n",
    "  __shared__ float K_j[B_c][d];\n",
    "  __shared__ float V_j[B_c][d];\n",
    "  \n",
    "  __shared__ float S_i[B_r][B_c];\n",
    "\n",
    "  float l_i[o_per_thread_x];\n",
    "  float m_i[o_per_thread_x];\n",
    "  float O_i[o_per_thread_x][o_per_thread_y];\n",
    "\n",
    "  for (int i = 0; i < T_r; i++) {\n",
    "    for (int ii = 0; ii < o_per_thread_x; ii++) {\n",
    "      for (int dd = 0; dd < o_per_thread_y; dd++) {\n",
    "        O_i[ii][dd] = 0;\n",
    "      }\n",
    "      l_i[ii] = 0.f;\n",
    "      m_i[ii] = NEG_INFINITY;\n",
    "    }\n",
    "    for (int ii = tid_y; ii < B_r; ii += blockDim.y) {\n",
    "      for (int dd = tid_x; dd < d; dd += blockDim.x) {\n",
    "         Q_i[ii][dd] = Q[batch_offset + (ii + i * B_r) * d + dd];\n",
    "      }\n",
    "    }\n",
    "    for (int j=0; j < T_c; j++) {\n",
    "        __syncthreads();\n",
    "        for (int jj=tid_y; jj < B_c; jj+= blockDim.y) {\n",
    "            for (int dd=tid_x; dd < d; dd += blockDim.x) {\n",
    "                K_j[jj][dd] = K[batch_offset + (jj + j * B_c) * d + dd];\n",
    "                V_j[jj][dd] = V[batch_offset + (jj + j * B_c) * d + dd];\n",
    "            }\n",
    "        }\n",
    "        __syncthreads();\n",
    "        // S_i = scale * (Q_i @ K_j.T)\n",
    "        for (int ii = tid_x; ii < B_r; ii += blockDim.x) {\n",
    "            for (int jj = tid_y; jj < B_c; jj += blockDim.y) {\n",
    "                float S_ij = 0.f;\n",
    "                for (int dd = 0; dd < d; dd++) {\n",
    "                    S_ij += Q_i[ii][dd] * K_j[jj][dd];\n",
    "                }\n",
    "                S_ij = scaling * S_ij;\n",
    "                S_i[ii][jj] = S_ij;\n",
    "            }\n",
    "        }\n",
    "        __syncthreads();\n",
    "        for (int ii = 0; ii < o_per_thread_x; ii++) {\n",
    "            float m = m_i[ii];\n",
    "            float last_m = m;\n",
    "            for (int jj = 0; jj < B_c; jj += 1) {\n",
    "                if (m < S_i[ii * blockDim.x + tid_x][jj]) {\n",
    "                  m = S_i[ii * blockDim.x + tid_x][jj];\n",
    "                }\n",
    "            }\n",
    "            m_i[ii] = m;\n",
    "            float l = exp(last_m - m) * l_i[ii];\n",
    "            for (int dd = 0; dd < o_per_thread_y; dd++) {\n",
    "                O_i[ii][dd] *= exp(last_m - m);\n",
    "            }\n",
    "            \n",
    "            for (int jj = 0; jj < B_c; jj ++) {\n",
    "                float S_ij = exp(S_i[ii * blockDim.x + tid_x][jj] - m);\n",
    "                l += S_ij;\n",
    "                for (int dd = 0; dd < o_per_thread_y; dd++) {\n",
    "                    O_i[ii][dd] += S_ij * V_j[jj][dd * blockDim.y + tid_y];\n",
    "                }\n",
    "            }\n",
    "            l_i[ii] = l;\n",
    "\n",
    "       }\n",
    "    }\n",
    "    for (int ii = 0; ii < o_per_thread_x; ii++) {\n",
    "      for (int dd = 0; dd < o_per_thread_y; dd++) {\n",
    "        out[batch_offset + (ii * blockDim.x + tid_x + i * B_r) * d + dd * blockDim.y + tid_y] = O_i[ii][dd] / l_i[ii];\n",
    "        out_l[batch_offset / d +  ii * blockDim.x + tid_x + i * B_r] = l_i[ii];\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "\n",
    "cuda_flash_attention_kernel = compile_program_and_get_kernel(cuda_src, \"silly_attn\")\n",
    "\n",
    "cuda_flash_attention_kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf44f9b",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "source": [
    "With this, we need a wrapper that calls our kernel. This basically prepares inputs and allocates outputs, defines the block and grid layout and calls `launch_kernel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ef456ab",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.4305e-06, device='cuda:0')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cuda_python_flash_attention(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor, is_causal: bool = False, scale: float | None = None):\n",
    "    assert Q.device.type == 'cuda'\n",
    "    \n",
    "    if is_causal:\n",
    "        raise NotImplementedError(\"cuda_python_flash_attention is_causal=True is not implemented\")\n",
    "    \n",
    "    *batch, N_inp, d = K.shape\n",
    "    *_, N_out, _ = Q.shape\n",
    "\n",
    "    assert d == cuda_d\n",
    "    \n",
    "    Q_3d = Q.reshape(-1, N_out, d)\n",
    "    K_3d = K.reshape(-1, N_inp, d)\n",
    "    V_3d = V.reshape(-1, N_inp, d)\n",
    "    \n",
    "    blocks = Q_3d.shape[0]\n",
    "\n",
    "    # assert shape compat\n",
    "    \n",
    "    O = V.new_zeros(*batch, N_out, d)\n",
    "    L = V.new_zeros(*batch, N_out, 1)\n",
    "\n",
    "    O_3d = O.view(-1, N_out, d)\n",
    "    L_3d = L.view(-1, N_out, 1)\n",
    "    \n",
    "    T_c = (N_inp + cuda_B_c - 1) // cuda_B_c\n",
    "    T_r = (N_out + cuda_B_r - 1) // cuda_B_r\n",
    "\n",
    "    if scale is None:\n",
    "        scale = 1 / math.sqrt(d)\n",
    "\n",
    "    assert N_inp % cuda_B_r == 0  # TODO\n",
    "    assert N_out == N_inp  # TODO\n",
    "\n",
    "    GRID = (blocks,)\n",
    "    BLOCK = (32, 32)\n",
    "\n",
    "    launch_kernel(cuda_flash_attention_kernel, GRID, BLOCK, O_3d, L_3d, K_3d, Q_3d, V_3d, numpy.float32(scale), \n",
    "                  numpy.int32(N_inp * d), numpy.int32(T_r), numpy.int32(T_c))\n",
    "    return O, L\n",
    "\n",
    "\n",
    "Qc = Q.cuda()\n",
    "Kc = K.cuda()\n",
    "Vc = V.cuda()\n",
    "\n",
    "actual, _ = cuda_python_flash_attention(Qc, Kc, Vc)\n",
    "expected = torch.nn.functional.scaled_dot_product_attention(Qc, Kc, Vc)\n",
    "\n",
    "(actual - expected).abs().max()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce98d7",
   "metadata": {},
   "source": [
    "So how slow is it? Quite a lot slower. Depending on the input sizes, this can be an order of magnitude.\n",
    "But hey, it's an optimization opportunity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be789298",
   "metadata": {
    "hideCode": false,
    "hideOutput": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1 ms ± 911 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "14.9 ms ± 1.63 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit torch.nn.functional.scaled_dot_product_attention(Qc, Kc, Vc); torch.cuda.synchronize()\n",
    "%timeit cuda_python_flash_attention(Qc, Kc, Vc); torch.cuda.synchronize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2995e26-6aea-4c3d-b830-e0ac44ffe224",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Running our kernel in Thunder\n",
    "\n",
    "We want to have our kernel handle calls to `scalar_dot_product_attention` where it applies.\n",
    "Fortunately, this is much easier than getting the kernel itself.\n",
    "\n",
    "We start with having our own executor.\n",
    "\n",
    "## Create a thunder executor\n",
    "\n",
    "We create an `OperatorExecutor` and register it as a default executor.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d85f00b",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[attn_ex, sdpa, nvfuser]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import thunder\n",
    "\n",
    "attn_ex = thunder.extend.OperatorExecutor('attn_ex', version=0.01)\n",
    "thunder.add_default_executor(attn_ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f00672e-f88e-43a8-a9b6-dbda1f0f8e80",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Register our implementation as an operator\n",
    "\n",
    "The next thing we do is to register our implementation as an executor. We use our implementation above for the execution function (the `fn` parameter) and provide a short meta describing the result metadata as determined by the input metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17b1d735-1b03-4ae5-aaca-1cb151a7bd48",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def my_attn_meta(query, key, value, is_causal, scale):\n",
    "    return thunder.TensorProxy(like=query), thunder.TensorProxy(like=query, shape=(*query.shape[:1], 1))\n",
    "\n",
    "my_attn = attn_ex.register_operator('my_attn', meta=my_attn_meta, fn=cuda_python_flash_attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae94b369-5bde-4784-8de2-13c3543e65cf",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Register our attention as an implementation of torch sdpa\n",
    "\n",
    "But to have Thunder automatically use our implementation we need to tell it that it implements sdpa.\n",
    "Our checker function takes the same arguments as PyTorch sdpa and returns `True` or `False` depending on whether our implementation applies. It makes sure we do not take variants we do not support in our implementation (non-cuda, or with causal or bespoke marking or dropout).\n",
    "\n",
    "The execution transform is also just a function that again takes the same inputs as PyTorch sdpa but has the same returns as well. The function itself is very basic, just calling the symbol we registered with Thunder.\n",
    "\n",
    "If we had a backward, we could not register a grad transform with `register_implementation` as well (see the [zero to thunder tutorial](./zero_to_thunder.ipynb) or the [extending thunder tutorial](./dev_tutorials/extend.ipynb) for an example with grad transform.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77d1e584-af9c-4d18-9136-d40d645435cb",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def my_attn_checker(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):\n",
    "    if attn_mask is not None or dropout_p != 0.0 or is_causal:\n",
    "        return False\n",
    "    return (query.device.type == 'cuda' and \n",
    "            key.device == query.device and\n",
    "            value.device == query.device)\n",
    "\n",
    "def my_attn_transform(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None):\n",
    "    if scale is None:\n",
    "        d = query.shape[-1]\n",
    "        scale = d**(-0.5)\n",
    "    out = my_attn(query, key, value, is_causal, scale)\n",
    "    return out[0]\n",
    "\n",
    "\n",
    "attn_ex.register_implementation(thunder.torch.scaled_dot_product_attention, checker=my_attn_checker,\n",
    "                                execution_transform=my_attn_transform)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23bf790e-afdc-4dcb-9a67-c5ea3ca54f8a",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Run...\n",
    "\n",
    "Now we are ready to run models with our implementation. To keep things simple, we just use a function calling the PyTorch attention function, but you could also use your favourite LLM from [LitGPT](https://github.com/Lightning-AI/litgpt) here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ddab1c9",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.4305e-06, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "def test_fn(query, key, value):\n",
    "    return torch.nn.functional.scaled_dot_product_attention(query, key, value, is_causal=False)\n",
    "\n",
    "jfn = thunder.jit(test_fn)\n",
    "\n",
    "print((jfn(Qc, Kc, Vc) - test_fn(Qc, Kc, Vc)).abs().max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ee118e",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Inspect\n",
    "\n",
    "Using `thunder.last_traces` we can look at what happened. The last trace in the list returned by this function is the fully transformed program and uses our new function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "efa29992",
   "metadata": {
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed by Delete Last Used (took 0 milliseconds)\n",
      "import torch\n",
      "from thunder.executors.torchex import no_autocast\n",
      "\n",
      "@torch.no_grad()\n",
      "@no_autocast\n",
      "def computation(query, key, value):\n",
      "  # query: \"cuda:0 f32[96, 512, 128]\"\n",
      "  # key: \"cuda:0 f32[96, 512, 128]\"\n",
      "  # value: \"cuda:0 f32[96, 512, 128]\"\n",
      "  (t13, _) = my_attn(query, key, value, False, 0.08838834764831845)\n",
      "  del query, key, value\n",
      "  return t13\n"
     ]
    }
   ],
   "source": [
    "print(thunder.last_traces(jfn)[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "062ca49f",
   "metadata": {},
   "source": [
    "The first trace contains the program as captured by Thunder, so it still has the call to PyTorch SDPA that is then translated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44378e68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import thunder\n",
      "import thunder.torch as ltorch\n",
      "import torch\n",
      "from thunder.executors.torchex import no_autocast\n",
      "\n",
      "@torch.no_grad()\n",
      "@no_autocast\n",
      "def computation(query, key, value):\n",
      "  # query: \"cuda:0 f32[96, 512, 128]\"\n",
      "  # key: \"cuda:0 f32[96, 512, 128]\"\n",
      "  # value: \"cuda:0 f32[96, 512, 128]\"\n",
      "  t13 = ltorch.scaled_dot_product_attention(query, key, value, None, 0.0, False, scale=None)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "    # t0 = ltorch.mul(query, 0.29730177875068026)  # t0: \"cuda:0 f32[96, 512, 128]\"\n",
      "      # t0 = prims.mul(query, 0.29730177875068026)  # t0: \"cuda:0 f32[96, 512, 128]\"\n",
      "    # t1 = ltorch.transpose(key, -2, -1)  # t1: \"cuda:0 f32[96, 128, 512]\"\n",
      "      # t1 = prims.transpose(key, (0, 2, 1))  # t1: \"cuda:0 f32[96, 128, 512]\"\n",
      "    # t2 = ltorch.mul(t1, 0.29730177875068026)  # t2: \"cuda:0 f32[96, 128, 512]\"\n",
      "      # t2 = prims.mul(t1, 0.29730177875068026)  # t2: \"cuda:0 f32[96, 128, 512]\"\n",
      "    # t3 = ltorch.matmul(t0, t2)  # t3: \"cuda:0 f32[96, 512, 512]\"\n",
      "      # t3 = prims.matmul(t0, t2)  # t3: \"cuda:0 f32[96, 512, 512]\"\n",
      "    # t12 = ltorch.softmax(t3, -1, dtype=None)  # t12: \"cuda:0 f32[96, 512, 512]\"\n",
      "      # t5 = ltorch.amax(t3, -1, True)  # t5: \"cuda:0 f32[96, 512, 1]\"\n",
      "        # t4 = prims.amax(t3, (2,))  # t4: \"cuda:0 f32[96, 512]\"\n",
      "        # t5 = prims.broadcast_in_dim(t4, [96, 512, 1], [0, 1])  # t5: \"cuda:0 f32[96, 512, 1]\"\n",
      "      # t7 = ltorch.sub(t3, t5, alpha=None)  # t7: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t6 = prims.broadcast_in_dim(t5, (96, 512, 512), (0, 1, 2))  # t6: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t7 = prims.sub(t3, t6)  # t7: \"cuda:0 f32[96, 512, 512]\"\n",
      "      # t8 = ltorch.exp(t7)  # t8: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t8 = prims.exp(t7)  # t8: \"cuda:0 f32[96, 512, 512]\"\n",
      "      # t10 = ltorch.sum(t8, -1, True, dtype=None)  # t10: \"cuda:0 f32[96, 512, 1]\"\n",
      "        # t9 = prims.sum(t8, (2,))  # t9: \"cuda:0 f32[96, 512]\"\n",
      "        # t10 = prims.broadcast_in_dim(t9, [96, 512, 1], [0, 1])  # t10: \"cuda:0 f32[96, 512, 1]\"\n",
      "      # t12 = ltorch.true_divide(t8, t10)  # t12: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t11 = prims.broadcast_in_dim(t10, (96, 512, 512), (0, 1, 2))  # t11: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t12 = prims.div(t8, t11)  # t12: \"cuda:0 f32[96, 512, 512]\"\n",
      "    # t13 = ltorch.matmul(t12, value)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "      # t13 = prims.matmul(t12, value)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "  return t13\n"
     ]
    }
   ],
   "source": [
    "print(thunder.last_traces(jfn)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af18944",
   "metadata": {},
   "source": [
    "# Comparing implementations\n",
    "\n",
    "If we want to compare implementations, we can also compile the function without our executor to get the \"default\" implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "edf2d53b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.4305e-06, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "jfn_without_attn_ex = thunder.jit(test_fn, executors=[thunder.sdpa_executor, thunder.nvfuser_executor])\n",
    "\n",
    "print((jfn(Qc, Kc, Vc) - jfn_without_attn_ex(Qc, Kc, Vc)).abs().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "edda6d3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.9 ms ± 37.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "1.12 ms ± 1.2 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit jfn(Qc, Kc, Vc) ; torch.cuda.synchronize()\n",
    "%timeit jfn_without_attn_ex(Qc, Kc, Vc) ; torch.cuda.synchronize()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f1b20d",
   "metadata": {},
   "source": [
    "And, of course, we can also see this by inspecting the traces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bf869e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed by Delete Last Used (took 0 milliseconds)\n",
      "import torch\n",
      "import torch.nn.functional\n",
      "from thunder.executors.torchex import no_autocast\n",
      "\n",
      "@torch.no_grad()\n",
      "@no_autocast\n",
      "def computation(query, key, value):\n",
      "  # query: \"cuda:0 f32[96, 512, 128]\"\n",
      "  # key: \"cuda:0 f32[96, 512, 128]\"\n",
      "  # value: \"cuda:0 f32[96, 512, 128]\"\n",
      "  t13 = torch.nn.functional.scaled_dot_product_attention(query, key, value, None, 0.0, False, scale=None)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "    # t13 = ltorch.scaled_dot_product_attention(query, key, value, None, 0.0, False, scale=None)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "      # t14 = ltorch.mul(query, 0.29730177875068026)  # t14: \"cuda:0 f32[96, 512, 128]\"\n",
      "        # t14 = prims.mul(query, 0.29730177875068026)  # t14: \"cuda:0 f32[96, 512, 128]\"\n",
      "      # t15 = ltorch.transpose(key, -2, -1)  # t15: \"cuda:0 f32[96, 128, 512]\"\n",
      "        # t15 = prims.transpose(key, (0, 2, 1))  # t15: \"cuda:0 f32[96, 128, 512]\"\n",
      "      # t16 = ltorch.mul(t15, 0.29730177875068026)  # t16: \"cuda:0 f32[96, 128, 512]\"\n",
      "        # t16 = prims.mul(t15, 0.29730177875068026)  # t16: \"cuda:0 f32[96, 128, 512]\"\n",
      "      # t17 = ltorch.matmul(t14, t16)  # t17: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t17 = prims.matmul(t14, t16)  # t17: \"cuda:0 f32[96, 512, 512]\"\n",
      "      # t26 = ltorch.softmax(t17, -1, dtype=None)  # t26: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t19 = ltorch.amax(t17, -1, True)  # t19: \"cuda:0 f32[96, 512, 1]\"\n",
      "          # t18 = prims.amax(t17, (2,))  # t18: \"cuda:0 f32[96, 512]\"\n",
      "          # t19 = prims.broadcast_in_dim(t18, [96, 512, 1], [0, 1])  # t19: \"cuda:0 f32[96, 512, 1]\"\n",
      "        # t21 = ltorch.sub(t17, t19, alpha=None)  # t21: \"cuda:0 f32[96, 512, 512]\"\n",
      "          # t20 = prims.broadcast_in_dim(t19, (96, 512, 512), (0, 1, 2))  # t20: \"cuda:0 f32[96, 512, 512]\"\n",
      "          # t21 = prims.sub(t17, t20)  # t21: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t22 = ltorch.exp(t21)  # t22: \"cuda:0 f32[96, 512, 512]\"\n",
      "          # t22 = prims.exp(t21)  # t22: \"cuda:0 f32[96, 512, 512]\"\n",
      "        # t24 = ltorch.sum(t22, -1, True, dtype=None)  # t24: \"cuda:0 f32[96, 512, 1]\"\n",
      "          # t23 = prims.sum(t22, (2,))  # t23: \"cuda:0 f32[96, 512]\"\n",
      "          # t24 = prims.broadcast_in_dim(t23, [96, 512, 1], [0, 1])  # t24: \"cuda:0 f32[96, 512, 1]\"\n",
      "        # t26 = ltorch.true_divide(t22, t24)  # t26: \"cuda:0 f32[96, 512, 512]\"\n",
      "          # t25 = prims.broadcast_in_dim(t24, (96, 512, 512), (0, 1, 2))  # t25: \"cuda:0 f32[96, 512, 512]\"\n",
      "          # t26 = prims.div(t22, t25)  # t26: \"cuda:0 f32[96, 512, 512]\"\n",
      "      # t13 = ltorch.matmul(t26, value)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "        # t13 = prims.matmul(t26, value)  # t13: \"cuda:0 f32[96, 512, 128]\"\n",
      "  del query, key, value\n",
      "  return t13\n"
     ]
    }
   ],
   "source": [
    "print(thunder.last_traces(jfn_without_attn_ex)[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63ff2eb",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "So that is it.\n",
    "\n",
    "What did we achieve?\n",
    "\n",
    "- We implemented a kernel following the flash-attention 2 pseudocode (but not having all the optimizations) in CUDA.\n",
    "- Then we looked at how to compile and run it using the NVIDIA CUDA-Python bindings.\n",
    "- Finally, we saw how Thunder executors make it easy run PyTorch programs with targeted optimizations.\n",
    "\n",
    "We hope that you do great things and may your kernels always turn out to be faster than the baseline!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a41ebe4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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