GEMV 4: Parameters

Parameter values are compile-time constants, which implies that the compiler is fully aware of their precise value. This enables the programmer to not just change the program’s behavior at runtime, but it also enables the programmer to change the program’s compilation.

Continuing on from the previous example, we add two compile-time parameters to the layout.csl file that specify the dimensions M and N of our problem, instead of hardcoding them in pe_program.csl. When the program is compiled, the program specifies M and N in the compile command. layout.csl also sets these parameter values in pe_program.csl in its @set_tile_code call.


See GEMV Tutorial 4: Parameters for a step-by-step walkthrough of this example.


param M: i16;
param N: i16;

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

layout {
  @set_rectangle(1, 1);
  @set_tile_code(0, 0, "pe_program.csl", .{
    .memcpy_params = memcpy.get_params(0),
    .M = M,
    .N = N

  // export symbol names
  @export_name("A", [*]f32, true);
  @export_name("x", [*]f32, true);
  @export_name("b", [*]f32, true);
  @export_name("y", [*]f32, false);
  @export_name("init_and_compute", fn()void);


param memcpy_params: comptime_struct;

// Matrix dimensions
param M: i16;
param N: i16;

// memcpy module provides infrastructure for copying data
// and launching functions from the host
const sys_mod = @import_module("<memcpy/memcpy>", memcpy_params);

// 48 kB of global memory contain A, x, b, y
var A: [M*N]f32; // A is stored row major
var x: [N]f32;
var b: [M]f32;
var y = @zeros([M]f32); // Initialize y to zero

// DSDs for accessing A, b, y
// A_dsd accesses column of A
var A_dsd = @get_dsd(mem1d_dsd, .{ .tensor_access = |i|{M} -> A[i*N] });
var b_dsd = @get_dsd(mem1d_dsd, .{ .tensor_access = |i|{M} -> b[i] });
var y_dsd = @get_dsd(mem1d_dsd, .{ .tensor_access = |i|{M} -> y[i] });

// ptrs to A, x, b, y will be advertised as symbols to host
var A_ptr: [*]f32 = &A;
var x_ptr: [*]f32 = &x;
var b_ptr: [*]f32 = &b;
const y_ptr: [*]f32 = &y;

// Compute gemv
fn gemv() void {
  // Loop over all columns of A
  for (@range(i16, N)) |i| {
    // Calculate contribution to A*x from ith column of A, ith elem of x
    @fmacs(y_dsd, y_dsd, A_dsd, x[i]);
    // Move A_dsd to next column of A
    A_dsd = @increment_dsd_offset(A_dsd, 1, f32);
  // Add b to A*x
  @fadds(y_dsd, y_dsd, b_dsd);

// Call initialize and gemv functions
fn init_and_compute() void {

comptime {
  @export_symbol(A_ptr, "A");
  @export_symbol(x_ptr, "x");
  @export_symbol(b_ptr, "b");
  @export_symbol(y_ptr, "y");

#!/usr/bin/env cs_python

import argparse
import json
import numpy as np

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

# Read arguments
parser = argparse.ArgumentParser()
parser.add_argument('--name', help="the test compile output dir")
parser.add_argument('--cmaddr', help="IP:port for CS system")
args = parser.parse_args()

# Get matrix dimensions from compile metadata
with open(f"{}/out.json", encoding='utf-8') as json_file:
  compile_data = json.load(json_file)

N = int(compile_data['params']['N'])
M = int(compile_data['params']['M'])

# Construct A, x, b
A = np.arange(M*N, dtype=np.float32)
x = np.full(shape=N, fill_value=1.0, dtype=np.float32)
b = np.full(shape=M, fill_value=2.0, dtype=np.float32)

# Calculate expected y
y_expected = A.reshape(M,N)@x + b

# Construct a runner using SdkRuntime
runner = SdkRuntime(, cmaddr=args.cmaddr)

# Get symbols for A, b, x, y on device
A_symbol = runner.get_id('A')
x_symbol = runner.get_id('x')
b_symbol = runner.get_id('b')
y_symbol = runner.get_id('y')

# Load and run the program

# Copy A, x, b to device
runner.memcpy_h2d(A_symbol, A, 0, 0, 1, 1, M*N, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)
runner.memcpy_h2d(x_symbol, x, 0, 0, 1, 1, N, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)
runner.memcpy_h2d(b_symbol, b, 0, 0, 1, 1, M, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)

# Launch the init_and_compute function on device
runner.launch('init_and_compute', nonblock=False)

# Copy y back from device
y_result = np.zeros([M], dtype=np.float32)
runner.memcpy_d2h(y_result, y_symbol, 0, 0, 1, 1, M, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)

# Stop the program

# Ensure that the result matches our expectation
np.testing.assert_allclose(y_result, y_expected, atol=0.01, rtol=0)

#!/usr/bin/env bash

set -e

cslc --arch=wse2 ./layout.csl --fabric-dims=8,3 \
--fabric-offsets=4,1 --params=M:4,N:6 -o out --memcpy --channels 1
cs_python --name out