Topic 1: Arrays and Pointers¶

Arrays can only be passed to or returned from functions used at compile-time. For functions used at runtime, pointers should be used instead. This example demonstrates a function incrementAndSum(), which accepts a pointer to an array and a pointer to a scalar. When declaring an array pointer, CSL requires that the type specification contain the size of the array. CSL does not have a null pointer.

Pointers are dereferenced using the .* syntax. Once dereferenced, they can be used just like non-pointer variables like (dataPtr.*)[0] for indexing into the first element of the array.

layout.csl¶

// The core kernel must start at P4.1 so the memcpy infrastructure has enough
// resources to route the data between the host and the device.

// color/ task ID map
//
//  ID var                ID var         ID var                ID var
//   0                     9             18                    27 reserved (memcpy)
//   1 MEMCPY_D2H_DATA_1  10             19                    28 reserved (memcpy)
//   2                    11             20                    29 reserved
//   3                    12             21 reserved (memcpy)  30 reserved (memcpy)
//   4                    13             22 reserved (memcpy)  31 reserved
//   5                    14             23 reserved (memcpy)  32
//   6                    15             24                    33
//   7                    16             25                    34
//   8 main_task_id       17             26                    35

// IDs for memcpy streaming colors
param MEMCPYD2H_DATA_1_ID: i16;

// Colors
const MEMCPYD2H_DATA_1: color = @get_color(MEMCPYD2H_DATA_1_ID);

// Task IDs
const main_task_id: local_task_id = @get_local_task_id(8);

const memcpy = @import_module( "<memcpy/get_params>", .{
  .width = 1,
  .height = 1,
  .MEMCPYD2H_1 = MEMCPYD2H_DATA_1
});

layout {
  @set_rectangle(1, 1);

  @set_tile_code(0, 0, "pe_program.csl", .{
    .memcpy_params = memcpy.get_params(0),
    .main_task_id = main_task_id
  });

  // export symbol name
  @export_name("f_run", fn()void);
}

pe_program.csl¶

// Not a complete program; the top-level source file is layout.csl

param memcpy_params: comptime_struct;

// Task IDs
param main_task_id: local_task_id;

const sys_mod = @import_module( "<memcpy/memcpy>", memcpy_params);

const dsd = @get_dsd(fabout_dsd, .{
   .extent = 1,
   .fabric_color = sys_mod.MEMCPYD2H_1
});


fn incrementAndSum(dataPtr: *[3]i16, resultPtr: *i16) void {
  // Write an updated value to each element of the array
  (dataPtr.*)[0] += 1;
  (dataPtr.*)[1] += 1;
  (dataPtr.*)[2] += 1;

  // Read all array values, sum them, and write the result
  resultPtr.* = (dataPtr.*)[0] + (dataPtr.*)[1] + (dataPtr.*)[2];
}

task mainTask() void {
  var result:i16 = 0;
  var data = [3]i16 { 1, 2, 3 };

  incrementAndSum(&data, &result);
  @mov16(dsd, result);
}

fn f_run() void {
  @activate(main_task_id);

  // RPC returns early before the data is sent out via D2H color
  // The host must wait for streaming D2H

  // WARNING: the user must unblock cmd color for every PE
  sys_mod.unblock_cmd_stream();
}

comptime {
  @bind_local_task(mainTask, main_task_id);

  @export_symbol(f_run);
}

run.py¶

#!/usr/bin/env cs_python

import argparse
import json
import numpy as np

from cerebras.sdk.sdk_utils import memcpy_view
from cerebras.sdk.runtime.sdkruntimepybind import SdkRuntime, MemcpyDataType # pylint: disable=no-name-in-module
from cerebras.sdk.runtime.sdkruntimepybind import MemcpyOrder # pylint: disable=no-name-in-module

parser = argparse.ArgumentParser()
parser.add_argument('--name', help='the test name')
parser.add_argument("--cmaddr", help="IP:port for CS system")
args = parser.parse_args()
dirname = args.name

# Parse the compile metadata
with open(f"{dirname}/out.json", encoding="utf-8") as json_file:
  compile_data = json.load(json_file)
params = compile_data["params"]
MEMCPYD2H_DATA_1 = int(params["MEMCPYD2H_DATA_1_ID"])
print(f"MEMCPYD2H_DATA_1 = {MEMCPYD2H_DATA_1}")

memcpy_dtype = MemcpyDataType.MEMCPY_16BIT
runner = SdkRuntime(dirname, cmaddr=args.cmaddr)

runner.load()
runner.run()

print("step 1: call f_run to send global of type i16 via streaming D2H")
runner.launch("f_run", nonblock=False)

print("step 2: streaming D2H")
# The D2H buffer must be of type u32
out_tensors_u32 = np.zeros(1, np.uint32)
runner.memcpy_d2h(out_tensors_u32, MEMCPYD2H_DATA_1, 0, 0, 1, 1, 1, \
    streaming=True, data_type=memcpy_dtype, order=MemcpyOrder.COL_MAJOR, nonblock=False)
# remove upper 16-bit of each u32
result_tensor = memcpy_view(out_tensors_u32, np.dtype(np.int16))

runner.stop()

# Ensure that the result matches our expectation
np.testing.assert_equal(result_tensor, [9])
print("SUCCESS!")

commands.sh¶

#!/usr/bin/env bash

set -e

cslc ./layout.csl --fabric-dims=8,3 \
--fabric-offsets=4,1 -o out \
--params=MEMCPYD2H_DATA_1_ID:1 \
--memcpy --channels=1 --width-west-buf=0 --width-east-buf=0
cs_python run.py --name out

SDK Documentation (1.1.0)

Topic 1: Arrays and Pointers

Contents

Topic 1: Arrays and Pointers¶

layout.csl¶

pe_program.csl¶

run.py¶

commands.sh¶