notebooks/IPU Peak Flops.ipynb

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   "metadata": {},
   "source": [
    "Copyright (c) 2023 Graphcore Ltd. All rights reserved."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "002700b7",
   "metadata": {},
   "source": [
    "# IPU Peak teraFlops\n",
    "\n",
    "This notebook shows how you can reach the maximum usages of IPU flops in a couple of lines of Python."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "108c181b",
   "metadata": {},
   "source": [
    "## Environment setup\n",
    "\n",
    "To run the demo using IPU hardware, you need to have the Poplar SDK enabled {and a PopTorch/TensorFlow wheel installed}. Refer to the [Getting Started guide](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started) for your system for details on how to do this. Also refer to the [Jupyter Quick Start guide](https://docs.graphcore.ai/projects/jupyter-notebook-quick-start/en/latest/index.html) for how to set up Jupyter to be able to run this notebook on a remote IPU machine."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5f856b38",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f14b0107",
   "metadata": {},
   "source": [
    "Import dependencies and define configuration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "17226e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax.config import config\n",
    "\n",
    "# Select how many IPUs will be visible.\n",
    "config.FLAGS.jax_ipu_device_count = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d8870fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.lax\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdc49153",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "174d33ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IpuDevice(id=0, num_tiles=1472, version=ipu2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Need real IPU hardware!\n",
    "d = jax.devices(\"ipu\")[0]\n",
    "assert not d.is_ipu_model\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf6e85a4",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "1850000000.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "num_tiles = d.num_tiles\n",
    "tiles = tuple(range(num_tiles))\n",
    "d.tile_clock_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08afe55c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86825ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tessellate_ipu.tile import IpuConvVertexType, ipu_cycle_count, ipu_cycle_count_overhead, tile_map, tile_put_sharded\n",
    "\n",
    "\n",
    "def tile_basic_matmul(lhs, rhs):\n",
    "    \"\"\"Run a basic matmul on every tile, using the `ConvPartial1x1` vertex.\n",
    "\n",
    "    Most optimal hardware usage: loading a small weight matrix once in CCCS\n",
    "    registers, and then performing a \"long\" AMP pipeline of size N.\n",
    "\n",
    "    Args:\n",
    "        lhs: [N, 8/16] array\n",
    "        rhs: [8/16, 8/16] array.\n",
    "    Returns:\n",
    "        Matmul result. [N, 8/16]\n",
    "    \"\"\"\n",
    "    accumulator_dtype = lhs.dtype\n",
    "    # accumulator_dtype = np.float32\n",
    "    output = tile_map(\n",
    "        jax.lax.dot_general_p,\n",
    "        lhs,\n",
    "        rhs,\n",
    "        dimension_numbers=(([1], [1]), ([], [])),\n",
    "        precision=jax.lax.Precision.DEFAULT,\n",
    "        preferred_element_type=accumulator_dtype,\n",
    "        ipu_vertex_type=IpuConvVertexType.ConvPartial1x1,\n",
    "    )\n",
    "    return output\n",
    "\n",
    "\n",
    "def tile_benchmark_matmul(lhs, rhs):\n",
    "    \"\"\"Benchmarking matmul IPU.\"\"\"\n",
    "    lhs = tile_put_sharded(lhs, tiles)\n",
    "    rhs = tile_put_sharded(rhs, tiles)\n",
    "    # Get IPU raw cycle before and after matmul\n",
    "    lhs, rhs, start = ipu_cycle_count(lhs, rhs)\n",
    "    out = tile_basic_matmul(lhs, rhs)\n",
    "    out, end = ipu_cycle_count(out)\n",
    "    return out, start, end\n",
    "\n",
    "\n",
    "# CPU jitting to double check the result!\n",
    "tile_benchmark_matmul_ipu = jax.jit(tile_benchmark_matmul, backend=\"ipu\")\n",
    "tile_benchmark_matmul_cpu = jax.jit(tile_benchmark_matmul, backend=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e2b5bfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_tile_matmul_flops(N, dtype):\n",
    "    \"\"\"Measure IPU flops usage by running small matmuls on every tile.\"\"\"\n",
    "    dtype = np.float16\n",
    "    lhs_size = N\n",
    "    rhs_size = 16\n",
    "    contract_size = 16\n",
    "\n",
    "    lhs_data = np.random.randn(len(tiles), lhs_size, contract_size).astype(dtype)\n",
    "    rhs_data = np.random.randn(len(tiles), rhs_size, contract_size).astype(dtype)\n",
    "    # Run independent matmuls on every tile, and measure cycle count.\n",
    "    _, start, end = tile_benchmark_matmul_ipu(lhs_data, rhs_data)\n",
    "    tile_cycle_count = np.asarray(end.array) - np.asarray(start.array)\n",
    "    cycle_count = np.mean(tile_cycle_count[:, 0]) - ipu_cycle_count_overhead()\n",
    "    # Flops conversion.\n",
    "    execution_time = cycle_count / d.tile_clock_frequency\n",
    "    # Count product & sum in matmul as floating point operation.\n",
    "    num_ops = 2 * lhs_size * rhs_size * contract_size * num_tiles\n",
    "    tflops = num_ops / execution_time * 1e-12\n",
    "    return cycle_count, tflops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6d0851f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matrix sizes to check!\n",
    "max_size = 1024 * 2\n",
    "step_size = 16\n",
    "sizes = np.arange(16, max_size + step_size, step_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1b5acf11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IPU Mk2 peak tflops.\n",
    "peak_tflops = d.tile_clock_frequency * num_tiles * 128 * 1e-12\n",
    "peak_tflops = np.array([peak_tflops for _ in sizes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "338d72b9",
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [09:17<00:00,  4.36s/it]\n"
     ]
    }
   ],
   "source": [
    "cycle_counts = []\n",
    "tflops = []\n",
    "\n",
    "for s in tqdm(sizes):\n",
    "    c, f = measure_tile_matmul_flops(s, np.float16)\n",
    "    cycle_counts.append(c)\n",
    "    tflops.append(f)\n",
    "\n",
    "cycle_counts = np.asarray(cycle_counts)\n",
    "tflops = np.asarray(tflops)"
   ]
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   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "# Peak TFlops.\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=sizes,\n",
    "        y=peak_tflops,\n",
    "        mode=\"lines\",\n",
    "        name=\"IPU Mk2 Peak TFlops\",\n",
    "        line=go.scatter.Line(dash=\"dot\"),\n",
    "        showlegend=True,\n",
    "    )\n",
    ")\n",
    "# FP16 TFlops.\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=sizes,\n",
    "        y=tflops,\n",
    "        mode=\"markers\",\n",
    "        name=\"FP16[N,16] @ FP16[16,16] -> FP16\",\n",
    "        marker=go.scatter.Marker(size=5),\n",
    "        showlegend=True,\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.update_layout(\n",
    "    font_family=\"Courier New\",\n",
    "    title_text=\"IPU on-tile AMP micro-benchmarking (TFLOPS)\",\n",
    "    xaxis_title=\"N (input shape: [N,16])\",\n",
    "    yaxis_title=\"TFLOPS\",\n",
    ")\n",
    "fig.update_xaxes(tickvals=[16, 64, 128, 256, 512, 768, 1024, 1536, 2048])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": []
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