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[examples/python/pytorch] added batchnorm cpp extension

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This commit is contained in:
Philippe Tillet
2019-07-09 20:59:04 -07:00
parent b7986baffa
commit 63b249c1d6
6 changed files with 214 additions and 206 deletions
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93
examples/python/pytorch/conv.cpp
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@@ -12,16 +12,6 @@
#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
typedef std::tuple<int32_t, int32_t, int32_t, int32_t, int32_t,
int32_t, int32_t, int32_t, int32_t,
int32_t, int32_t, int32_t,
int32_t, int32_t, int32_t,
triton::dnn::conv::type, bool> conv_key_t;
static std::map<CUstream, std::unique_ptr<triton::driver::stream>> m_stream;
static std::map<conv_key_t, std::unique_ptr<triton::jit>> m_jit;
static std::map<conv_key_t, std::unique_ptr<triton::dnn::conv>> m_config;
torch::Tensor conv_common(
int32_t B, int32_t C, int32_t D, int32_t H, int32_t W,
int32_t T, int32_t R, int32_t S, int32_t NF,
@@ -31,95 +21,34 @@ torch::Tensor conv_common(
torch::Tensor torcha, torch::Tensor torchb, torch::Tensor torchbias,
bool autotune = false
) {
// Wrap CUDA handles
c10::DeviceIndex device = torcha.storage().device().index();
// Get stream
CUstream custream = (CUstream)at::cuda::getCurrentCUDAStream(device).stream();
triton::driver::stream* stream;
if(m_stream.find(custream) == m_stream.end())
stream = m_stream.emplace(custream, new triton::driver::cu_stream(custream, false)).first->second.get();
else
stream = m_stream.at(custream).get();
// Get context
triton::driver::context* ctx = stream->context();
// Get configuration
triton::driver::cu_stream stream(custream, false);
triton::driver::context* ctx = stream.context();
// Get template
bool has_bias = torchbias.storage().size() > 0;
conv_key_t key = {B, C, D, H, W, T, R, S, NF, stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, ty, has_bias};
triton::dnn::conv* configuration;
if(m_config.find(key) == m_config.end())
configuration = m_config.emplace(key, new triton::dnn::conv(
B, C, D, H, W, T, R, S, NF,
stride_d, stride_h, stride_w,
pad_d, pad_h, pad_w,
1, 1, 1,
"fp32", "fp32", ty, has_bias)).first->second.get();
else
configuration = m_config.at(key).get();
triton::dnn::conv conv(B, C, D, H, W, T, R, S, NF,
stride_d, stride_h, stride_w,
pad_d, pad_h, pad_w,
1, 1, 1,
"fp32", "fp32", ty, has_bias);
// Bind memory
triton::driver::cu_buffer a(ctx, (CUdeviceptr)torcha.storage().data(), false);
triton::driver::cu_buffer b(ctx, (CUdeviceptr)torchb.storage().data(), false);
triton::driver::cu_buffer cubias(ctx, (CUdeviceptr)torchbias.storage().data(), false);
triton::driver::buffer* bias = has_bias ? &cubias : nullptr;
// Allocate output
std::vector<int32_t> c_shapes = configuration->c_shapes();
std::vector<int32_t> c_shapes = conv.c_shapes();
torch::Tensor torchc;
if(ty == triton::dnn::conv::WGRAD)
torchc = torch::empty({c_shapes[0], c_shapes[2], c_shapes[3], c_shapes[4]}, torch::kFloat).cuda();
else
torchc = torch::empty({c_shapes[0], c_shapes[1], c_shapes[3], c_shapes[4]}, torch::kFloat).cuda();
triton::driver::cu_buffer c(ctx, (CUdeviceptr)torchc.storage().data(), false);
// Get JIT
triton::jit* jit;
if(m_jit.find(key) == m_jit.end()){
jit = m_jit.emplace(key, new triton::jit(ctx)).first->second.get();
std::ostringstream oss;
configuration->src(oss);
std::string src = oss.str();
// benchmark a given convolution kernel
auto benchmark = [&](triton::driver::kernel* kernel,
triton::jit::launch_information info) {
configuration->init(stream, (triton::driver::cu_module*)kernel->module());
unsigned TM = info.global_range_size[0];
unsigned TN = info.global_range_size[1];
unsigned nthreads = info.num_threads;
unsigned GZ = jit->get_int("GZ");
configuration->enqueue(stream, kernel, &a, &b, &c, bias, TM, TN, GZ, nthreads);
stream->synchronize();
double ts = triton::tools::bench([&](){ configuration->enqueue(stream, kernel, &a, &b, &c, bias, TM, TN, GZ, nthreads); },
[&](){ stream->synchronize(); }, stream->context()->device());
return configuration->get_nflops() / ts * 1e-3;
};
// auto-tune and save result
if(autotune) {
triton::jit::tune_res_t best = jit->autotune("conv", src.c_str(), benchmark);
jit->add_module("conv", src.c_str(), best.params);
}
else {
jit->add_module("conv", src.c_str(), configuration->default_params());
}
triton::driver::kernel* kernel = jit->get_function("conv");
configuration->init(stream, (triton::driver::cu_module*)kernel->module());
}
else
jit = m_jit.at(key).get();
// Run
triton::driver::kernel* kernel = jit->get_function("conv");
triton::jit::launch_information info = jit->get_launch_info("conv");
unsigned GZ = jit->get_int("GZ");
// launch info
unsigned TM = info.global_range_size[0];
unsigned TN = info.global_range_size[1];
unsigned nthreads = info.num_threads;
// enqueue
configuration->enqueue(stream, kernel, &a, &b, &c, bias, TM, TN, GZ, nthreads);
// Enqueue
conv.enqueue(&stream, {&a, &b, &c, bias});
return torchc;
}