public void CanComposeFunction()
{
Device device = new Device(testContext);
string code = @"function (I) -> (O) {
O = I * I;
}";
Function f = new Function(testContext, code);
Assert.True(f.IsAllocated);
DeviceTensor i = new DeviceTensor(device, new Compiler.PlaidML.Shape(testContext, PlaidmlDatatype.PLAIDML_DATA_INT32, 6),
"I");
DeviceTensor o = new DeviceTensor(device, new Compiler.PlaidML.Shape(testContext, PlaidmlDatatype.PLAIDML_DATA_INT32, 6),
"O");
Int32[] input_data = { 0, 1, 3, 4, 5, 6 };
i.CreateView <Int32>(MemoryMapType.Discard).CopyFromAndFree(input_data);
var v = i.CreateView <Int32>(MemoryMapType.Retain);
Assert.Equal(3, v[2]);
Invoker <int> inv1 = new Invoker <int>(testContext, f, o, i);
Assert.True(inv1.IsAllocated);
Invocation <int> k = inv1.Invoke();
Assert.True(k.IsAllocated);
DeviceTensorView <Int32> R = o.CreateView <Int32>(MemoryMapType.Retain);
Assert.Equal(6, R.ElementCount);
Assert.Equal(9, R[2]);
DeviceTensor co = new DeviceTensor(device, new Compiler.PlaidML.Shape(testContext, PlaidmlDatatype.PLAIDML_DATA_INT32, 6),
"O");
Composer c = new Composer(testContext);
Applier a = new Applier(testContext, f);
a.AddInputValue(i);
var fo = a.AddOutputValue("O");
Assert.True(fo.IsAllocated);
Assert.True(c.AddInputPlaceholder("I", i.DimensionCount));
Assert.True(c.AddOutputValue(co));
Assert.True(c.AddDependency(a));
Assert.True(c.AddUpdate(co, fo));
Function gf = c.BuildFunction();
Assert.True(gf.IsAllocated);
Invoker <int> gi = new Invoker <int>(testContext, gf, co, i);
Assert.True(gi.IsAllocated);
Invocation <int> inv = gi.Invoke();
Assert.True(inv.IsAllocated);
R = co.CreateView <Int32>(MemoryMapType.Retain);
Assert.Equal(6, R.ElementCount);
Assert.Equal(9, R[2]);
}
public void CanComputeGradient()
{
Device device = new Device(testContext);
string code = @"function (I) -> (O) {
O = I * I;
}";
Function f = new Function(testContext, code);
Assert.True(f.IsAllocated);
DeviceTensor i = new DeviceTensor(device, new Compiler.PlaidML.Shape(testContext, PlaidmlDatatype.PLAIDML_DATA_INT32, 6),
"I");
DeviceTensor o = new DeviceTensor(device, new Compiler.PlaidML.Shape(testContext, PlaidmlDatatype.PLAIDML_DATA_INT32, 6),
"O");
Int32[] input_data = { 0, 1, 3, 4, 5, 6 };
i.CreateView <Int32>(MemoryMapType.Discard).CopyFromAndFree(input_data);
var v = i.CreateView <Int32>(MemoryMapType.Retain);
Assert.Equal(3, v[2]);
Invoker <int> inv1 = new Invoker <int>(testContext, f, o, i);
Assert.True(inv1.IsAllocated);
Invocation <int> k = inv1.Invoke();
Assert.True(k.IsAllocated);
DeviceTensorView <Int32> R = o.CreateView <Int32>(MemoryMapType.Retain);
Assert.Equal(6, R.ElementCount);
Assert.Equal(9, R[2]);
var gradients = new Dictionary <DeviceTensor, Value>();
for (int n = 0; n < inv1.InputTensors.Count; n++)
{
Gradient gc = new Gradient(testContext, inv1.OutputValue);
if (!gc.IsAllocated)
{
continue;
}
Value grad = gc.ComputeWrt(inv1.InputTensors[n]);
if (!grad.IsAllocated)
{
continue;
}
gradients.Add(inv1.InputTensors[n], grad);
}
Composer c = new Composer(testContext);
foreach (DeviceTensor t in inv1.InputTensors)
{
Assert.True(c.AddInputPlaceholder(t.Name, t.DimensionCount));
}
List <DeviceTensor> gradTensors = new List <DeviceTensor>();
foreach (var g in gradients)
{
var gt = new DeviceTensor(g.Key.Device, g.Key.Shape, g.Value.Name);
c.AddOutputValue(gt);
c.AddUpdate(gt, g.Value);
gradTensors.Add(gt);
}
Function gf = c.BuildFunction();
Assert.True(gf.IsAllocated);
Invoker <int> ginv = new Invoker <int>(testContext, gf, inv1.InputTensors.ToArray(), gradTensors.ToArray());
Assert.True(ginv.IsAllocated);
var k2 = ginv.Invoke();
Assert.True(k2.IsAllocated);
DeviceTensorView <Int32> RG = gradTensors.First().CreateView <Int32>(MemoryMapType.Retain);
}