GEMV 2: Memory DSDs¶

Continuing on from the previous example, we now extend it by introducing memory Data Structure Descriptors (DSDs), an efficient mechanism for performing operations on entire tensors.

This program creates three one-dimensional memory DSDs for accessing A, b, and y, each of which specifies how to loop over the respective arrays. The tensor_access field specifies an induction variable, a loop bound, and an affine expression (i.e., a linear function plus a constant) to generate various addresses at runtime.

b_dsd and y_dsd access the M contiguous elements of b and y, respectively. A_dsd accesses M elements of A, but strided by N elements. Because A is stored in row major format, this means that A_dsd initially accesses the 0th column of A.

These DSDs are used by the DSD operations @fmacs and @fadds to compute Ax + b and store it in y. The gemv function first loops over N, with the @fmacs in iteration i computing the scalar-vector product of x[i] with column i of A, and incrementing y by that result. The increment_dsd_offset operation updates A_dsd by shifting its access by one element. This causes A_dsd to access the next column of A. After the loop, y is incremented by b with the @fadds operation, to complete the computation.

Other DSD operations and their associated operand types are described in Builtins for DSD Operations.

Note

See GEMV Tutorial 2: Memory DSDs for a step-by-step walkthrough of this example.

layout.csl¶

const memcpy = @import_module("<memcpy_multi/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) });

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

pe_program.csl¶

param memcpy_params: comptime_struct;

const LAUNCH: color = @get_color(8);

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

// Constants definining dimensions of our matrix
const M: i16 = 4;
const N: i16 = 6;

// 48 kB of global memory contain A, x, b, y
var A: [M*N]f32; // A is stored row major

// Initialize x, b, y using builtins
var x = @constants([N]f32, 1.0);
var b = @constants([M]f32, 2.0);
var y = @zeros([M]f32);

// DSDs for accessing A, b, y
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] });

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

// ptr to y will be advertised as symbol to host
const y_ptr: [*]f32 = &y;

// Initialize A matrix
fn initialize() void {
  // for loop with range syntax
  for (@range(i16, M*N)) |idx| {
    A[idx] = @as(f32, idx);
  }
}

// Compute gemv
fn gemv() void {
  // Loop over all columns of A
  for (@range(u16, 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]);
    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 {
  initialize();
  gemv();
  sys_mod.unblock_cmd_stream();
}

comptime {
  @export_symbol(y_ptr, "y");
  @export_symbol(init_and_compute);
  @rpc(LAUNCH);
}

run.py¶

#!/usr/bin/env cs_python

import argparse
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()

# Matrix dimensions
M = 4
N = 6

# Construct A, x, b
A = np.arange(M*N, dtype=np.float32).reshape(M, N)
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@x + b

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

# Get symbol for copying y result off device
y_symbol = runner.get_id('y')

# Load and run the program
runner.load()
runner.run()

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

# Copy y back from device
y_result = np.zeros([1*1*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
runner.stop()

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

commands.sh¶

#!/usr/bin/env bash

set -e

cslc ./layout.csl --fabric-dims=8,3 \
--fabric-offsets=4,1 -o out --memcpy --channels 1
cs_python run.py --name out

SDK Documentation (0.9.0)

GEMV 2: Memory DSDs

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