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#include <array>
#include <future>
#include <memory>
#include <random>
#include "gpu.h"
#include "utils/array_utils.h"
#include "utils/logging.h"
#include "llmc/reference_impls.h"
#include "shaders.h"
using namespace gpu;
void testResidual(Context &ctx) {
constexpr size_t N = 200000;
constexpr size_t workgroupSize = 256;
std::array<float, N> input1Arr;
std::array<float, N> input2Arr;
range(input1Arr);
range(input2Arr);
std::array<float, N> outputArr;
Tensor input1 = createTensor(ctx, {N}, kf32, input1Arr.data());
Tensor input2 = createTensor(ctx, {N}, kf32, input2Arr.data());
Tensor output = createTensor(ctx, {N}, kf32, outputArr.data());
std::promise<void> promise;
std::future<void> future = promise.get_future();
KernelCode shaderCode = {kShaderResidual, workgroupSize, kf32};
LOG(kDefLog, kInfo, "Shader Code :\n%s", shaderCode.data.c_str());
Kernel op =
createKernel(ctx, {kShaderResidual, workgroupSize, kf32},
Bindings{input1, input2, output}, {cdiv(N, workgroupSize), 1, 1});
dispatchKernel(ctx, op, promise);
wait(ctx, future);
toCPU(ctx, output, outputArr.data(), sizeof(outputArr));
LOG(kDefLog, kInfo, "%s",
show<float, N, 1>(outputArr, "Residual Output").c_str());
std::array<float, N> outputRef;
ref::residual_forward_cpu(outputRef.data(), input1Arr.data(),
input2Arr.data(), N);
LOG(kDefLog, kInfo, "%s",
show<float, N, 1>(outputRef, "Residual Reference Output").c_str());
assert(isclose(outputArr.data(), outputRef.data(), N));
LOG(kDefLog, kInfo, "Done with Residual Test");
}
void testHadamard(Context &ctx) {
constexpr size_t N = 200000;
constexpr size_t workgroupSize = 256;
std::array<float, N> input1Arr;
std::array<float, N> input2Arr;
range(input1Arr);
range(input2Arr);
std::array<float, N> outputArr;
Tensor input1 = createTensor(ctx, {N}, kf32, input1Arr.data());
Tensor input2 = createTensor(ctx, {N}, kf32, input2Arr.data());
Tensor output = createTensor(ctx, {N}, kf32, outputArr.data());
KernelCode shaderCode = {kShaderHadamard, workgroupSize, kf32};
LOG(kDefLog, kInfo, "Shader Code :\n%s", shaderCode.data.c_str());
std::promise<void> promise;
std::future<void> future = promise.get_future();
Kernel op =
createKernel(ctx, {kShaderHadamard, workgroupSize, kf32},
Bindings{input1, input2, output}, {cdiv(N, workgroupSize), 1, 1});
dispatchKernel(ctx, op, promise);
wait(ctx, future);
LOG(kDefLog, kInfo, "%s",
show<float, N, 1>(outputArr, "Hadamard Output").c_str());
}
void testMatmul(Context &ctx) {
static constexpr size_t M = 4;
static constexpr size_t K = 5;
static constexpr size_t N = 4;
auto gen = std::mt19937(31415);
std::array<float, M * K> input1Arr;
std::array<float, K * N> input2Arr;
std::array<float, M * N> outputArr;
randint(input1Arr, gen, 0, 5);
range(input2Arr);
Tensor input1 = createTensor(ctx, {M, K}, kf32, input1Arr.data());
Tensor input2 = createTensor(ctx, {K, N}, kf32, input2Arr.data());
Tensor output = createTensor(ctx, {M, N}, kf32, outputArr.data());
Kernel op = createKernel(
ctx, MatmulShader(256, kShaderMatMul1, kf32, M, K, N),
Bindings{input1, input2, output}, {cdiv(M * N, 256), 1, 1});
std::promise<void> promise;
std::future<void> future = promise.get_future();
dispatchKernel(ctx, op, promise);
wait(ctx, future);
toCPU(ctx, output, outputArr.data(), sizeof(outputArr));
LOG(kDefLog, kInfo, "%s", show<float, M, K>(input1Arr, "A").c_str());
LOG(kDefLog, kInfo, "%s", show<float, K, N>(input2Arr, "B").c_str());
LOG(kDefLog, kInfo, "%s", show<float, M, N>(outputArr, "C").c_str());
std::array<float, M * N> refOutputArr;
std::array<float, K * N> input2ArrT;
transpose(input2Arr.data(), input2ArrT.data(), K, N);
LOG(kDefLog, kInfo, "%s", show<float, N, K>(input2ArrT, "B'").c_str());
ref::matmul_forward_cpu(refOutputArr.data(), input1Arr.data(),
input2ArrT.data(), nullptr, 1, M, K, N);
LOG(kDefLog, kInfo, show<float, M, N>(refOutputArr, "C (reference)").c_str());
LOG(kDefLog, kInfo, "Done with Matmul Test");
bool passed = isclose(outputArr.data(), refOutputArr.data(), N);
assert(passed);
}
void testTensorPool(Context &ctx) {
LOG(kDefLog, kInfo, "Starting Tensor Pool Test");
// Test using the tensor pool to prepare tensor buffers for kernel invocation
TensorPool pool = ctx.pool;
std::array<float, 6> inputArr = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
Tensor input = createTensor(ctx, {2, 3}, kf32, inputArr.data());
Tensor output = createTensor(ctx, {2, 3}, kf32);
for (int i = 0; i < 10; i++) {
Tensor t = createTensor(ctx, {2, 3}, kf32);
}
// initializing a gpu buffer w/ value and then copy it back to CPU
std::array<float, 6> initValue = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0};
LOG(kDefLog, kInfo, "making tensors with init");
Tensor tInit = createTensor(ctx, {2, 3}, kf32, initValue.data());
LOG(kDefLog, kInfo, "Done with Tensor Pool Test");
std::array<float, 6> targetValue;
toCPU(ctx, tInit, targetValue.data(), sizeof(initValue));
LOG(kDefLog, kInfo, "%s",
show<float, 2, 3>(initValue, "initialized GPU value").c_str());
LOG(kDefLog, kInfo, "%s",
show<float, 2, 3>(targetValue, "To CPU from GPU").c_str());
LOG(kDefLog, kInfo, "Done with Tensor Pool Test");
}
void testGelu(Context &ctx) {
static constexpr size_t N = 3072;
std::array<float, N> inputArr;
// range(inputArr);
auto gen = std::mt19937(31415);
// TODO(avh): investigate - on metal tanh seems to produce nan for values > 10
randint(inputArr, gen, 0, 10); // for debugging
std::array<float, N> outputArr;
Tensor geluIn = createTensor(ctx, {N}, kf32, inputArr.data());
Tensor geluOut = createTensor(ctx, {N}, kf32, outputArr.data());
LOG(kDefLog, kInfo, "Creating GELU Shader");
KernelCode shader = {kShaderGelu, 256, kf32};
Kernel op = createKernel(ctx, shader, Bindings{geluIn, geluOut},
{cdiv(N, 256), 1, 1});
LOG(kDefLog, kInfo, "Workgroup size: %s",
toString(shader.workgroupSize).c_str());
LOG(kDefLog, kInfo, "dispatching GELU Shader");
std::promise<void> promise;
std::future<void> future = promise.get_future();
dispatchKernel(ctx, op, promise);
wait(ctx, future);
toCPU(ctx, geluOut, outputArr.data(), sizeof(outputArr));
LOG(kDefLog, kInfo, "%s", show<float, N, 1>(inputArr, "GELU Input").c_str());
LOG(kDefLog, kInfo, "%s",
show<float, N, 1>(outputArr, "GELU Output").c_str());
std::array<float, N> refOutputArr;
ref::gelu_forward_cpu(refOutputArr.data(), inputArr.data(), N);
LOG(kDefLog, kInfo, "%s",
show<float, N, 1>(refOutputArr, "GELU Reference Output").c_str());
bool passed = isclose(outputArr.data(), refOutputArr.data(), N);
assert(passed);
LOG(kDefLog, kInfo, "Gelu passed? %d", passed);
LOG(kDefLog, kInfo, "Done with Gelu Test");
}
void testLayerNorm(Context &ctx) {
struct LNParam {
uint32_t N; // check
uint32_t C;
};
constexpr size_t N = 6;
constexpr size_t C = 3072;
std::mt19937 gen(31415);
std::array<float, N * C> inputArr;
randint(inputArr, gen, 0, 3);
// range(inputArr);
std::array<float, N * C> outputArr;
std::array<float, C> weightArr;
std::array<float, C> biasArr;
Tensor input = createTensor(ctx, {N, C}, kf32, inputArr.data());
LNParam params = {N, C};
randint(weightArr, gen, 0, 5); // populate randomly
randint(biasArr, gen, 0, 5);
Tensor weight = createTensor(ctx, {C}, kf32, weightArr.data());
Tensor bias = createTensor(ctx, {C}, kf32, biasArr.data());
Tensor output = createTensor(ctx, {N, C}, kf32, outputArr.data());
std::promise<void> promise;
std::future<void> future = promise.get_future();
Kernel op = createKernel(ctx, {kShaderLayerNorm1, 256, kf32},
Bindings{input, weight, bias, output},
/* n threads */ {N, 1, 1}, params);
dispatchKernel(ctx, op, promise);
wait(ctx, future);
toCPU(ctx, output, outputArr.data(), sizeof(outputArr));
LOG(kDefLog, kInfo, "%s",
show<float, N, C>(inputArr, "LayerNorm Input").c_str());
LOG(kDefLog, kInfo, "%s",
show<float, 1, C>(weightArr, "LayerNorm Weight").c_str());
LOG(kDefLog, kInfo, "%s",
show<float, 1, C>(biasArr, "LayerNorm Bias").c_str());
LOG(kDefLog, kInfo, "%s",
show<float, N, C>(outputArr, "LayerNorm Output").c_str());
std::array<float, N * C> refOutputArr;
ref::layernorm_forward_cpu(refOutputArr.data(), inputArr.data(),
weightArr.data(), biasArr.data(), N, 1, C);
LOG(kDefLog, kInfo, "%s",
show<float, N, C>(refOutputArr,
"LayerNorm Reference Implementation Output")
.c_str());
bool passed = isclose(outputArr.data(), refOutputArr.data(), N * C);
assert(passed);
LOG(kDefLog, kInfo, "LayerNorm passed? %d", passed);
LOG(kDefLog, kInfo, "Done with LayerNorm Test");
}
void testSoftmax(Context &ctx) {
struct SoftmaxParam {
uint32_t N;
uint32_t C;
};
static constexpr size_t B = 6; // batch size
static constexpr size_t T = 8; // token index
static constexpr size_t C = 3072; // input channels
std::array<float, B * T * C> inputArr;
std::array<float, B * T * C> outputArr;
std::mt19937 gen(31415);
randint(inputArr, gen, 0, 3);
Tensor input = createTensor(ctx, {B * T, C}, kf32, inputArr.data());
Tensor output = createTensor(ctx, {B * T, C}, kf32, outputArr.data());
LOG(kDefLog, kInfo, "num threads: %d", B * T);
std::promise<void> promise;
std::future<void> future = promise.get_future();
Kernel op = createKernel(
ctx, {kShaderSoftmax1, 256, kf32}, Bindings{input, output},
Shape{cdiv(B * T, 256), 1, 1}, SoftmaxParam{B * T, C});
dispatchKernel(ctx, op, promise);
wait(ctx, future);
toCPU(ctx, output, outputArr.data(), sizeof(outputArr));
LOG(kDefLog, kInfo, "%s",
show<float, B * T, C>(inputArr, "Softmax Input").c_str());
LOG(kDefLog, kInfo, "%s",
show<float, B * T, C>(outputArr, "Softmax Output").c_str());
std::array<float, B * T * C> refOutputArr;
ref::softmax_forward_cpu(refOutputArr.data(), inputArr.data(), B * T, C);
LOG(kDefLog, kInfo, "%s",
show<float, B * T, C>(refOutputArr, "Softmax reference Output").c_str());
LOG(kDefLog, kInfo, "number of elements: %d", B * T * C);
bool passed = isclose(outputArr.data(), refOutputArr.data(), B * T * C);
assert(passed);
LOG(kDefLog, kInfo, "Softmax passed? %d", passed);
LOG(kDefLog, kInfo, "Done with Softmax Test");
}
void testAttention(Context &ctx) {
static constexpr size_t B = 6;
static constexpr size_t T = 32; // token index
static constexpr size_t C = 3072; // input channels
static constexpr size_t QKV_DIM = 256;
static constexpr size_t N_HEADS = 12;
static constexpr size_t OC =
QKV_DIM * N_HEADS * 3; // output channels, 3 for Q, K, V
std::array<float, B * T * C> inputArr;
std::array<float, B * OC> outputArr;
std::array<float, C * OC> weightArr;
}
int main(int argc, char **argv) {
Context ctx = createContext();
testTensorPool(ctx);
testResidual(ctx);
testHadamard(ctx);
testMatmul(ctx);
testGelu(ctx);
testLayerNorm(ctx);
testSoftmax(ctx);
LOG(kDefLog, kInfo, "Done with all tests");
}
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