weblas
Version:
GPU accelerated BLAS for node and the browser
420 lines (316 loc) • 9.9 kB
JavaScript
var tape = require('tape'),
weblas = require('../index'),
loader = require('floader'); // browserify aware file loader (xhr in browser)
weblas.test = require('../lib/test');
var RTOL = 1e-05,
ATOL = 1e-12;
// reusing data from sscal
var dataDirectory = 'test/data/sscal/',
testFile = 'small.json';
var gl = weblas.gpu.gl;
var matrixFiles = ['a.arr'];
tape("Tensor.transpose: 3 x 3", function(t){
t.plan(2);
var x = new Float32Array([ 1.0, 2.0, 3.0,
5.0, 6.0, 7.0,
9.0, 10.0, 11.0]),
expected = new Float32Array([ 1.0, 5.0, 9.0,
2.0, 6.0, 10.0,
3.0, 7.0, 11.0]);
var M = 3, N = 3,
t0 = new weblas.pipeline.Tensor([M, N], x),
t1;
try{
// when tranposing texture for t0 is deleted by default
t1 = t0.transpose();
// when transfering texture for t1 is deleted by default
var result = t1.transfer(true);
}
catch(ex){
t.assert(false, ex);
return;
}
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
// use internals to check that texture is padded correctly
var pad = 1,
padded;
try{
padded = weblas.test.padData(N, M, pad, expected);
out = weblas.gpu.gl.createOutputTexture(N, M + pad);
// float extraction
weblas.gpu.encode(N, M + pad, t1.texture, out);
result = new Float32Array(weblas.gpu.gl.readData(N, M + pad));
weblas.gpu.gl.context.deleteTexture(out);
}
catch(ex){
t.assert(false, ex);
}
weblas.test.assert.allclose(t, result, padded, null, RTOL, ATOL);
t1.delete();
});
tape("Tensor.transpose: 3 x 4", function(t){
t.plan(2);
var x = new Float32Array([ 1.0, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0]),
expected = new Float32Array([ 1.0, 5.0, 9.0,
2.0, 6.0, 10.0,
3.0, 7.0, 11.0,
4.0, 8.0, 12.0]);
var M = 3, N = 4,
t0 = new weblas.pipeline.Tensor([M, N], x),
t1;
try{
// when tranposing texture for t0 is deleted by default
t1 = t0.transpose();
// when transfering texture for t1 is deleted by default
var result = t1.transfer(true);
}
catch(ex){
t.assert(false, ex);
return;
}
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
// use internals to check that texture is padded correctly
var pad = 1,
padded;
try{
padded = weblas.test.padData(N, M, pad, expected);
out = weblas.gpu.gl.createOutputTexture(N, M + pad);
// float extraction
weblas.gpu.encode(N, M + pad, t1.texture, out);
result = new Float32Array(weblas.gpu.gl.readData(N, M + pad));
weblas.gpu.gl.context.deleteTexture(out);
}
catch(ex){
t.assert(false, ex);
}
weblas.test.assert.allclose(t, result, padded, null, RTOL, ATOL);
t1.delete();
});
function generateTestCase(prefix, M, N){
return function(t){
var pad = weblas.gpu.gl.getPad(M);
if(pad == 0){
t.plan(1);
} else {
t.plan(2);
}
var X, expected; // typed arrays
// directory containing matrix data files for current test
var testDirectory = dataDirectory + prefix + '/';
// load matrices from files
weblas.test.load(testDirectory, matrixFiles, function(err, matrices){
// matrices is an array which matches matrixFiles
var X = matrices[0];
if(!(X && X.length && X.length == M * N)){
throw new Error("malformed data");
}
expected = weblas.util.transpose(M, N, X);
var t0 = new weblas.pipeline.Tensor([M, N], X),
t1;
try{
// when tranposing texture for t0 is deleted by default
t1 = t0.transpose();
// when transfering texture for t1 is deleted by default
var result = t1.transfer(true);
}
catch(ex){
t.assert(false, ex);
return;
}
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
if(pad > 0){
// use internals to check that texture is padded correctly
var padded;
try{
padded = weblas.test.padData(N, M, pad, expected);
out = weblas.gpu.gl.createOutputTexture(N, M + pad);
// float extraction
weblas.gpu.encode(N, M + pad, t1.texture, out);
result = new Float32Array(weblas.gpu.gl.readData(N, M + pad));
weblas.gpu.gl.context.deleteTexture(out);
}
catch(ex){
t.assert(false, ex);
}
weblas.test.assert.allclose(t, result, padded, null, RTOL, ATOL);
t1.delete();
}
});
};
}
loader.load(dataDirectory + testFile, function(err, config){
var suite = JSON.parse(config);
// suite configuration file uses directory name as key
for(var i = 0; i < suite.length; i++){
directory = String("0000" + (i + 1)).slice(-4);
var test = suite[i];
var input = test['in'],
sizes = input['shape'],
arg = test['arg'] || {};
var m = input[0]['shape'][0],
n = input[0]['shape'][1];
//console.log("a: " + a + "; b: " + b);
var testName = "Tensor.transpose: " + m + "x" + n;
tape(testName, generateTestCase(directory, m, n));
}
});
tape("Tensor.reshape: 4 x 8", function(t){
t.plan(1);
var x = new Float32Array([ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0,
25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0]),
expected = new Float32Array([ 1.0, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0,
13.0, 14.0, 15.0, 16.0,
17.0, 18.0, 19.0, 20.0,
21.0, 22.0, 23.0, 24.0,
25.0, 26.0, 27.0, 28.0,
29.0, 30.0, 31.0, 32.0]);
var M = 4, N = 8,
M2 = 8, N2 = 4,
t0 = new weblas.pipeline.Tensor([M, N], x),
t1;
try{
// when tranposing texture for t0 is deleted by default
t1 = t0.reshape([M2, N2]);
// when transfering texture for t1 is deleted by default
var result = t1.transfer();
}
catch(ex){
t.assert(false, ex);
return;
}
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
});
tape("Tensor.reshape: 3 x 4", function(t){
t.plan(2);
var x = new Float32Array([ 1.0, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0,
9.0, 10.0, 11.0, 12.0]),
expected = new Float32Array([ 1.0, 2.0, 3.0,
4.0, 5.0, 6.0,
7.0, 8.0, 9.0,
10.0, 11.0, 12.0]);
var M = 3, N = 4,
M2 = 4, N2 = 3,
t0 = new weblas.pipeline.Tensor([M, N], x),
t1;
try{
// when tranposing texture for t0 is deleted by default
t1 = t0.reshape([M2, N2]);
// when transfering texture for t1 is deleted by default
var result = t1.transfer(true);
}
catch(ex){
t.assert(false, ex);
return;
}
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
// use internals to check that texture is padded correctly
var pad = 1,
padded;
try{
padded = weblas.test.padData(M2, N2, pad, expected);
out = weblas.gpu.gl.createOutputTexture(M2, N2 + pad);
// float extraction
weblas.gpu.encode(M2, N2 + pad, t1.texture, out);
result = new Float32Array(weblas.gpu.gl.readData(M2, N2 + pad));
weblas.gpu.gl.context.deleteTexture(out);
}
catch(ex){
t.assert(false, ex);
}
weblas.test.assert.allclose(t, result, padded, null, RTOL, ATOL);
t1.delete();
});
function generateReshapeTestCase(prefix, M, N){
return function(t){
var pad = weblas.gpu.gl.getPad(M);
if(pad == 0){
t.plan(1);
} else {
t.plan(2);
}
var X, expected; // typed arrays
// directory containing matrix data files for current test
var testDirectory = dataDirectory + prefix + '/';
// load matrices from files
weblas.test.load(testDirectory, matrixFiles, function(err, matrices){
// matrices is an array which matches matrixFiles
var X = matrices[0];
if(!(X && X.length && X.length == M * N)){
throw new Error("malformed data");
}
expected = X;
var t0 = new weblas.pipeline.Tensor([M, N], X),
t1;
try{
// when tranposing texture for t0 is deleted by default
t1 = t0.reshape([N, M]);
// when transfering texture for t1 is deleted by default
var result = t1.transfer(true);
}
catch(ex){
t.assert(false, ex);
return;
}
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
if(pad > 0){
// use internals to check that texture is padded correctly
var padded;
try{
padded = weblas.test.padData(N, M, pad, expected);
out = weblas.gpu.gl.createOutputTexture(N, M + pad);
// float extraction
weblas.gpu.encode(N, M + pad, t1.texture, out);
result = new Float32Array(weblas.gpu.gl.readData(N, M + pad));
weblas.gpu.gl.context.deleteTexture(out);
}
catch(ex){
t.assert(false, ex);
}
weblas.test.assert.allclose(t, result, padded, null, RTOL, ATOL);
t1.delete();
}
});
};
}
loader.load(dataDirectory + testFile, function(err, config){
var suite = JSON.parse(config);
// suite configuration file uses directory name as key
for(var i = 0; i < suite.length; i++){
directory = String("0000" + (i + 1)).slice(-4);
var test = suite[i];
var input = test['in'],
sizes = input['shape'],
arg = test['arg'] || {};
var m = input[0]['shape'][0],
n = input[0]['shape'][1];
//console.log("a: " + a + "; b: " + b);
var testName = "Tensor.reshape: " + m + "x" + n;
tape(testName, generateReshapeTestCase(directory, m, n));
}
});
function testWithPad(t, M, N, pad, result, expected, texture, RTOL, ATOL){
weblas.test.assert.allclose(t, result, expected, null, RTOL, ATOL);
if(pad > 0){
// use internals to check that texture is padded correctly
var padded;
try{
padded = weblas.test.padData(M, N, pad, expected);
out = weblas.gpu.gl.createOutputTexture(M, N + pad);
// float extraction
weblas.gpu.encode(M, N + pad, texture, out);
result = new Float32Array(weblas.gpu.gl.readData(M, N + pad));
weblas.gpu.gl.context.deleteTexture(out);
}
catch(ex){
t.assert(false, ex);
}
weblas.test.assert.allclose(t, result, padded, null, RTOL, ATOL);
}
}