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rocm_jax/docs/sharded-computation.ipynb
Michael Hudgins 2e808f2836 Merge pull request #26279 from MichaelHudgins:tsan-resultstore 
PiperOrigin-RevId: 723918760
2025-02-06 14:55:57 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(sharded-computation)=\n",
"# Introduction to parallel programming\n",
"\n",
"<!--* freshness: { reviewed: '2024-05-10' } *-->\n",
"\n",
"This tutorial serves as an introduction to device parallelism for Single-Program Multi-Data (SPMD) code in JAX. SPMD is a parallelism technique where the same computation, such as the forward pass of a neural network, can be run on different input data (for example, different inputs in a batch) in parallel on different devices, such as several GPUs or Google TPUs.\n",
"\n",
"The tutorial covers three modes of parallel computation:\n",
"\n",
"- _Automatic parallelism via {func}`jax.jit`_: The compiler chooses the optimal computation strategy (a.k.a. \"the compiler takes the wheel\").\n",
"- _Semi-automated parallelism_ using {func}`jax.jit` and {func}`jax.lax.with_sharding_constraint`\n",
"- _Fully manual parallelism with manual control using {func}`jax.experimental.shard_map.shard_map`_: `shard_map` enables per-device code and explicit communication collectives\n",
"\n",
"Using these schools of thought for SPMD, you can transform a function written for one device into a function that can run in parallel on multiple devices.\n",
"\n",
"If you are running these examples in a Google Colab notebook, make sure that your hardware accelerator is the latest Google TPU by checking your notebook settings: **Runtime** > **Change runtime type** > **Hardware accelerator** > **TPU v2** (which provides eight devices to work with)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"outputId": "18905ae4-7b5e-4bb9-acb4-d8ab914cb456"
},
"outputs": [
{
"data": {
"text/plain": [
"[TpuDevice(id=0, process_index=0, coords=(0,0,0), core_on_chip=0),\n",
" TpuDevice(id=1, process_index=0, coords=(0,0,0), core_on_chip=1),\n",
" TpuDevice(id=2, process_index=0, coords=(1,0,0), core_on_chip=0),\n",
" TpuDevice(id=3, process_index=0, coords=(1,0,0), core_on_chip=1),\n",
" TpuDevice(id=4, process_index=0, coords=(0,1,0), core_on_chip=0),\n",
" TpuDevice(id=5, process_index=0, coords=(0,1,0), core_on_chip=1),\n",
" TpuDevice(id=6, process_index=0, coords=(1,1,0), core_on_chip=0),\n",
" TpuDevice(id=7, process_index=0, coords=(1,1,0), core_on_chip=1)]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import jax\n",
"jax.devices()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key concept: Data sharding\n",
"\n",
"Key to all of the distributed computation approaches below is the concept of *data sharding*, which describes how data is laid out on the available devices.\n",
"\n",
"How can JAX understand how the data is laid out across devices? JAX's datatype, the {class}`jax.Array` immutable array data structure, represents arrays with physical storage spanning one or multiple devices, and helps make parallelism a core feature of JAX. The {class}`jax.Array` object is designed with distributed data and computation in mind. Every `jax.Array` has an associated {mod}`jax.sharding.Sharding` object, which describes which shard of the global data is required by each global device. When you create a {class}`jax.Array` from scratch, you also need to create its `Sharding`.\n",
"\n",
"In the simplest cases, arrays are sharded on a single device, as demonstrated below:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"outputId": "39fdbb79-d5c0-4ea6-8b20-88b2c502a27a"
},
"outputs": [
{
"data": {
"text/plain": [
"{TpuDevice(id=0, process_index=0, coords=(0,0,0), core_on_chip=0)}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import jax.numpy as jnp\n",
"arr = jnp.arange(32.0).reshape(4, 8)\n",
"arr.devices()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"outputId": "536f773a-7ef4-4526-c58b-ab4d486bf5a1"
},
"outputs": [
{
"data": {
"text/plain": [
"SingleDeviceSharding(device=TpuDevice(id=0, process_index=0, coords=(0,0,0), core_on_chip=0))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.sharding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a more visual representation of the storage layout, the {mod}`jax.debug` module provides some helpers to visualize the sharding of an array. For example, {func}`jax.debug.visualize_array_sharding` displays how the array is stored in memory of a single device:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"outputId": "74a793e9-b13b-4d07-d8ec-7e25c547036d"
},
"outputs": [
{
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},
"metadata": {},
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],
"source": [
"jax.debug.visualize_array_sharding(arr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create an array with a non-trivial sharding, you can define a {mod}`jax.sharding` specification for the array and pass this to {func}`jax.device_put`.\n",
"\n",
"Here, define a {class}`~jax.sharding.NamedSharding`, which specifies an N-dimensional grid of devices with named axes, where {class}`jax.sharding.Mesh` allows for precise device placement:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"outputId": "0b397dba-3ddc-4aca-f002-2beab7e6b8a5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NamedSharding(mesh=Mesh('x': 2, 'y': 4), spec=PartitionSpec('x', 'y'))\n"
]
}
],
"source": [
"from jax.sharding import PartitionSpec as P\n",
"\n",
"mesh = jax.make_mesh((2, 4), ('x', 'y'))\n",
"sharding = jax.sharding.NamedSharding(mesh, P('x', 'y'))\n",
"print(sharding)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Passing this `Sharding` object to {func}`jax.device_put`, you can obtain a sharded array:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"outputId": "c8ceedba-05ca-4156-e6e4-1e98bb664a66"
},
"outputs": [
{
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"text": [
"[[ 0. 1. 2. 3. 4. 5. 6. 7.]\n",
" [ 8. 9. 10. 11. 12. 13. 14. 15.]\n",
" [16. 17. 18. 19. 20. 21. 22. 23.]\n",
" [24. 25. 26. 27. 28. 29. 30. 31.]]\n"
]
},
{
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"arr_sharded = jax.device_put(arr, sharding)\n",
"\n",
"print(arr_sharded)\n",
"jax.debug.visualize_array_sharding(arr_sharded)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UEObolTqw4pp"
},
"source": [
"The device numbers here are not in numerical order, because the mesh reflects the underlying toroidal topology of the device.\n",
"\n",
"The {class}`~jax.sharding.NamedSharding` includes a parameter called `memory_kind`. This parameter determines the type of memory to be used and defaults to `device`. You can set this parameter to `pinned_host` if you prefer to place it on the host.\n",
"\n",
"To create a new sharding that only differs from an existing sharding in terms of its memory kind, you can use the `with_memory_kind` method on the existing sharding."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aKNeOHTJnqmS",
"outputId": "847c53ec-8b2e-4be0-f993-7fde7d77c0f2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pinned_host\n",
"device\n"
]
}
],
"source": [
"s_host = jax.NamedSharding(mesh, P('x', 'y'), memory_kind='pinned_host')\n",
"s_dev = s_host.with_memory_kind('device')\n",
"arr_host = jax.device_put(arr, s_host)\n",
"arr_dev = jax.device_put(arr, s_dev)\n",
"print(arr_host.sharding.memory_kind)\n",
"print(arr_dev.sharding.memory_kind)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jDHYnVqHwaST"
},
"source": [
"## 1. Automatic parallelism via `jit`\n",
"\n",
"Once you have sharded data, the easiest way to do parallel computation is to simply pass the data to a {func}`jax.jit`-compiled function! In JAX, you need to only specify how you want the input and output of your code to be partitioned, and the compiler will figure out how to: 1) partition everything inside; and 2) compile inter-device communications.\n",
"\n",
"The XLA compiler behind `jit` includes heuristics for optimizing computations across multiple devices.\n",
"In the simplest of cases, those heuristics boil down to *computation follows data*.\n",
"\n",
"To demonstrate how auto-parallelization works in JAX, below is an example that uses a {func}`jax.jit`-decorated staged-out function: it's a simple element-wise function, where the computation for each shard will be performed on the device associated with that shard, and the output is sharded in the same way:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"outputId": "de46f86a-6907-49c8-f36c-ed835e78bc3d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shardings match: True\n"
]
}
],
"source": [
"@jax.jit\n",
"def f_elementwise(x):\n",
" return 2 * jnp.sin(x) + 1\n",
"\n",
"result = f_elementwise(arr_sharded)\n",
"\n",
"print(\"shardings match:\", result.sharding == arr_sharded.sharding)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As computations get more complex, the compiler makes decisions about how to best propagate the sharding of the data.\n",
"\n",
"Here, you sum along the leading axis of `x`, and visualize how the result values are stored across multiple devices (with {func}`jax.debug.visualize_array_sharding`):"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"outputId": "90c3b997-3653-4a7b-c8ff-12a270f11d02"
},
"outputs": [
{
"data": {
"text/html": [
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"<span style=\"color: #ffffff; text-decoration-color: #ffffff; background-color: #393b79\"> </span><span style=\"color: #ffffff; text-decoration-color: #ffffff; background-color: #de9ed6\"> </span><span style=\"color: #ffffff; text-decoration-color: #ffffff; background-color: #ad494a\"> </span><span style=\"color: #000000; text-decoration-color: #000000; background-color: #b5cf6b\"> </span>\n",
"</pre>\n"
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"\u001b[38;2;255;255;255;48;2;57;59;121m \u001b[0m\u001b[38;2;255;255;255;48;2;57;59;121mTPU 0,6\u001b[0m\u001b[38;2;255;255;255;48;2;57;59;121m \u001b[0m\u001b[38;2;255;255;255;48;2;222;158;214m \u001b[0m\u001b[38;2;255;255;255;48;2;222;158;214mTPU 1,7\u001b[0m\u001b[38;2;255;255;255;48;2;222;158;214m \u001b[0m\u001b[38;2;255;255;255;48;2;173;73;74m \u001b[0m\u001b[38;2;255;255;255;48;2;173;73;74mTPU 2,4\u001b[0m\u001b[38;2;255;255;255;48;2;173;73;74m \u001b[0m\u001b[38;2;0;0;0;48;2;181;207;107m \u001b[0m\u001b[38;2;0;0;0;48;2;181;207;107mTPU 3,5\u001b[0m\u001b[38;2;0;0;0;48;2;181;207;107m \u001b[0m\n",
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[48. 52. 56. 60. 64. 68. 72. 76.]\n"
]
}
],
"source": [
"@jax.jit\n",
"def f_contract(x):\n",
" return x.sum(axis=0)\n",
"\n",
"result = f_contract(arr_sharded)\n",
"jax.debug.visualize_array_sharding(result)\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q4N5mrr9i_ki"
},
"source": [
"The result is partially replicated: that is, the first two elements of the array are replicated on devices `0` and `6`, the second on `1` and `7`, and so on.\n",
"\n",
"### 1.1 Sharding transformation between memory types\n",
"\n",
"The output sharding of a {func}`jax.jit` function can differ from the input sharding if you specify the output sharding using the `out_shardings` parameter. Specifically, the `memory_kind` of the output can be different from that of the input array.\n",
"\n",
"#### Example 1: Pinned host to device memory\n",
"\n",
"In the example below, the {func}`jax.jit` function `f` takes an array sharded in `pinned_host` memory and generates an array in `device` memory."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PXu3MhafyRHo",
"outputId": "7bc6821f-a4a9-4cf8-8b21-e279d516d27b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 1. 2. 3. 4. 5. 6. 7.]\n",
" [ 8. 9. 10. 11. 12. 13. 14. 15.]\n",
" [16. 17. 18. 19. 20. 21. 22. 23.]\n",
" [24. 25. 26. 27. 28. 29. 30. 31.]]\n",
"device\n"
]
}
],
"source": [
"f = jax.jit(lambda x: x, out_shardings=s_dev)\n",
"out_dev = f(arr_host)\n",
"print(out_dev)\n",
"print(out_dev.sharding.memory_kind)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LuYFqpcBySiX"
},
"source": [
"#### Example 2: Device to pinned_host memory\n",
"\n",
"In the example below, the {func}`jax.jit` function `g` takes an array sharded in `device` memory and generates an array in `pinned_host` memory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qLsgNlKfybRw",
"outputId": "a16448b9-7e39-408f-b200-505f65ad4464"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 1. 2. 3. 4. 5. 6. 7.]\n",
" [ 8. 9. 10. 11. 12. 13. 14. 15.]\n",
" [16. 17. 18. 19. 20. 21. 22. 23.]\n",
" [24. 25. 26. 27. 28. 29. 30. 31.]]\n",
"pinned_host\n"
]
}
],
"source": [
"g = jax.jit(lambda x: x, out_shardings=s_host)\n",
"out_host = g(arr_dev)\n",
"print(out_host)\n",
"print(out_host.sharding.memory_kind)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7BGD31-owaSU"
},
"source": [
"## 2. Semi-automated sharding with constraints\n",
"\n",
"If you'd like to have some control over the sharding used within a particular computation, JAX offers the {func}`~jax.lax.with_sharding_constraint` function. You can use {func}`jax.lax.with_sharding_constraint` (in place of {func}`jax.device_put()`) together with {func}`jax.jit` for more control over how the compiler constraints how the intermediate values and outputs are distributed.\n",
"\n",
"For example, suppose that within `f_contract` above, you'd prefer the output not to be partially-replicated, but rather to be fully sharded across the eight devices:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"outputId": "8468f5c6-76ca-4367-c9f2-93c723687cfd"
},
"outputs": [
{
"data": {
"text/html": [
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"\u001b[38;2;255;255;255;48;2;57;59;121m \u001b[0m\u001b[38;2;255;255;255;48;2;214;97;107m \u001b[0m\u001b[38;2;255;255;255;48;2;140;162;82m \u001b[0m\u001b[38;2;255;255;255;48;2;222;158;214m \u001b[0m\u001b[38;2;0;0;0;48;2;231;203;148m \u001b[0m\u001b[38;2;255;255;255;48;2;107;110;207m \u001b[0m\u001b[38;2;255;255;255;48;2;165;81;148m \u001b[0m\u001b[38;2;255;255;255;48;2;140;109;49m \u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[48. 52. 56. 60. 64. 68. 72. 76.]\n"
]
}
],
"source": [
"@jax.jit\n",
"def f_contract_2(x):\n",
" out = x.sum(axis=0)\n",
" sharding = jax.sharding.NamedSharding(mesh, P('x'))\n",
" return jax.lax.with_sharding_constraint(out, sharding)\n",
"\n",
"result = f_contract_2(arr_sharded)\n",
"jax.debug.visualize_array_sharding(result)\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives you a function with the particular output sharding you'd like.\n",
"\n",
"## 3. Manual parallelism with `shard_map`\n",
"\n",
"In the automatic parallelism methods explored above, you can write a function as if you're operating on the full dataset, and `jit` will split that computation across multiple devices. By contrast, with {func}`jax.experimental.shard_map.shard_map` you write the function that will handle a single shard of data, and `shard_map` will construct the full function.\n",
"\n",
"`shard_map` works by mapping a function across a particular *mesh* of devices (`shard_map` maps over shards). In the example below:\n",
"\n",
"- As before, {class}`jax.sharding.Mesh` allows for precise device placement, with the axis names parameter for logical and physical axis names.\n",
"- The `in_specs` argument determines the shard sizes. The `out_specs` argument identifies how the blocks are assembled back together.\n",
"\n",
"**Note:** {func}`jax.experimental.shard_map.shard_map` code can work inside {func}`jax.jit` if you need it."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"outputId": "435c32f3-557a-4676-c11b-17e6bab8c1e2"
},
"outputs": [
{
"data": {
"text/plain": [
"Array([ 1. , 2.682942 , 2.818595 , 1.28224 , -0.513605 ,\n",
" -0.9178486 , 0.44116896, 2.3139732 , 2.9787164 , 1.824237 ,\n",
" -0.08804226, -0.99998045, -0.07314599, 1.8403342 , 2.9812148 ,\n",
" 2.3005757 , 0.42419332, -0.92279506, -0.50197446, 1.2997544 ,\n",
" 2.8258905 , 2.6733112 , 0.98229736, -0.69244075, -0.81115675,\n",
" 0.7352965 , 2.525117 , 2.912752 , 1.5418116 , -0.32726777,\n",
" -0.97606325, 0.19192469], dtype=float32)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from jax.experimental.shard_map import shard_map\n",
"mesh = jax.make_mesh((8,), ('x',))\n",
"\n",
"f_elementwise_sharded = shard_map(\n",
" f_elementwise,\n",
" mesh=mesh,\n",
" in_specs=P('x'),\n",
" out_specs=P('x'))\n",
"\n",
"arr = jnp.arange(32)\n",
"f_elementwise_sharded(arr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function you write only \"sees\" a single batch of the data, which you can check by printing the device local shape:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"outputId": "99a3dc6e-154a-4ef6-8eaa-3dd0b68fb1da"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"global shape: x.shape=(32,)\n",
"device local shape: x.shape=(4,)\n"
]
}
],
"source": [
"x = jnp.arange(32)\n",
"print(f\"global shape: {x.shape=}\")\n",
"\n",
"def f(x):\n",
" print(f\"device local shape: {x.shape=}\")\n",
" return x * 2\n",
"\n",
"y = shard_map(f, mesh=mesh, in_specs=P('x'), out_specs=P('x'))(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because each of your functions only \"sees\" the device-local part of the data, it means that aggregation-like functions require some extra thought.\n",
"\n",
"For example, here's what a `shard_map` of a {func}`jax.numpy.sum` looks like:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"outputId": "1e9a45f5-5418-4246-c75b-f9bc6dcbbe72"
},
"outputs": [
{
"data": {
"text/plain": [
"Array([ 6, 22, 38, 54, 70, 86, 102, 118], dtype=int32)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def f(x):\n",
" return jnp.sum(x, keepdims=True)\n",
"\n",
"shard_map(f, mesh=mesh, in_specs=P('x'), out_specs=P('x'))(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Your function `f` operates separately on each shard, and the resulting summation reflects this.\n",
"\n",
"If you want to sum across shards, you need to explicitly request it using collective operations like {func}`jax.lax.psum`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"outputId": "4fd29e80-4fee-42b7-ff80-29f9887ab38d"
},
"outputs": [
{
"data": {
"text/plain": [
"Array(496, dtype=int32)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def f(x):\n",
" sum_in_shard = x.sum()\n",
" return jax.lax.psum(sum_in_shard, 'x')\n",
"\n",
"shard_map(f, mesh=mesh, in_specs=P('x'), out_specs=P())(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the output no longer has a sharded dimension, set `out_specs=P()` (recall that the `out_specs` argument identifies how the blocks are assembled back together in `shard_map`).\n",
"\n",
"## Comparing the three approaches\n",
"\n",
"With these concepts fresh in our mind, let's compare the three approaches for a simple neural network layer.\n",
"\n",
"Start by defining your canonical function like this:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "1TdhfTsoiqS1"
},
"outputs": [],
"source": [
"@jax.jit\n",
"def layer(x, weights, bias):\n",
" return jax.nn.sigmoid(x @ weights + bias)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"outputId": "f3007fe4-f6f3-454e-e7c5-3638de484c0a"
},
"outputs": [
{
"data": {
"text/plain": [
"Array([0.02138912, 0.893112 , 0.59892005, 0.97742504], dtype=float32)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"rng = np.random.default_rng(0)\n",
"\n",
"x = rng.normal(size=(32,))\n",
"weights = rng.normal(size=(32, 4))\n",
"bias = rng.normal(size=(4,))\n",
"\n",
"layer(x, weights, bias)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can automatically run this in a distributed manner using {func}`jax.jit` and passing appropriately sharded data.\n",
"\n",
"If you shard the leading axis of both `x` and `weights` in the same way, then the matrix multiplication will automatically happen in parallel:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"outputId": "80be899e-8dbc-4bfc-acd2-0f3d554a0aa5"
},
"outputs": [
{
"data": {
"text/plain": [
"Array([0.02138912, 0.893112 , 0.59892005, 0.97742504], dtype=float32)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mesh = jax.make_mesh((8,), ('x',))\n",
"sharding = jax.sharding.NamedSharding(mesh, P('x'))\n",
"\n",
"x_sharded = jax.device_put(x, sharding)\n",
"weights_sharded = jax.device_put(weights, sharding)\n",
"\n",
"layer(x_sharded, weights_sharded, bias)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can use {func}`jax.lax.with_sharding_constraint` in the function to automatically distribute unsharded inputs:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"outputId": "bb63e8da-ff4f-4e95-f083-10584882daf4"
},
"outputs": [
{
"data": {
"text/plain": [
"Array([0.02138914, 0.89311206, 0.5989201 , 0.97742516], dtype=float32)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@jax.jit\n",
"def layer_auto(x, weights, bias):\n",
" x = jax.lax.with_sharding_constraint(x, sharding)\n",
" weights = jax.lax.with_sharding_constraint(weights, sharding)\n",
" return layer(x, weights, bias)\n",
"\n",
"layer_auto(x, weights, bias) # pass in unsharded inputs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, you can do the same thing with `shard_map`, using {func}`jax.lax.psum` to indicate the cross-shard collective required for the matrix product:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"outputId": "568d1c85-39a7-4dba-f09a-0e4f7c2ea918"
},
"outputs": [
{
"data": {
"text/plain": [
"Array([0.02138914, 0.89311206, 0.5989201 , 0.97742516], dtype=float32)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from functools import partial\n",
"\n",
"@jax.jit\n",
"@partial(shard_map, mesh=mesh,\n",
" in_specs=(P('x'), P('x', None), P(None)),\n",
" out_specs=P(None))\n",
"def layer_sharded(x, weights, bias):\n",
" return jax.nn.sigmoid(jax.lax.psum(x @ weights, 'x') + bias)\n",
"\n",
"layer_sharded(x, weights, bias)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"This tutorial serves as a brief introduction of sharded and parallel computation in JAX.\n",
"\n",
"To learn about each SPMD method in-depth, check out these docs:\n",
"- {doc}`../notebooks/Distributed_arrays_and_automatic_parallelization`\n",
"- {doc}`../notebooks/shard_map`"
]
}
],
"metadata": {
"accelerator": "TPU",
"colab": {
"gpuType": "V28",
"provenance": [],
"toc_visible": true
},
"jupytext": {
"formats": "ipynb,md:myst"
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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