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stuff

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This commit is contained in:
Philippe Tillet
2019-07-03 19:25:16 -07:00
parent 0d8faa5b1e
commit 1d88f0a36b
7 changed files with 194 additions and 61 deletions
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4
examples/cpp/shift.cpp
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@@ -74,12 +74,12 @@ int main() {
// shift
std::vector<unsigned> params = {
16, 4, 64, 16, 4, 128, 2, 2, 1, 2, 4, 4, 16, 4
4, 2, 16, 4, 128, 2, 2, 1, 1, 8, 16, 8, 2
};
std::ostringstream oss;
shift.src(oss);
std::string src = oss.str();
jit.autotune("shift", src.c_str(), benchmark);
// jit.autotune("shift", src.c_str(), benchmark);
jit.add_module("shift", src.c_str(), params);
triton::driver::kernel* kernel = jit.get_function("shift");
triton::jit::launch_information info = jit.get_launch_info("shift");

42
examples/python/tensorflow/run.py
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@@ -1,7 +1,9 @@
import os
import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np
from time import time
data_files_path = tf.resource_loader.get_data_files_path()
library_dir = os.path.dirname(os.path.realpath(__file__))
module = tf.load_op_library(os.path.join(library_dir, 'libtf_blocksparse.so'))
@@ -42,23 +44,45 @@ def run_conv():
result = sess.run([c], feed_dict = {a: ha,
b: hb})[0]
@ops.RegisterGradient('ShiftConv')
def blocksparse_matmul_grad(op, dy):
shift_h = op.get_attr('shift_h')
shift_w = op.get_attr('shift_w')
x = op.inputs[0]
w = op.inputs[1]
dx = module.shift_conv_dx(dy, w, shift_h=shift_h, shift_w=shift_w)
dw = module.shift_conv_dw(dy, x, shift_h=shift_h, shift_w=shift_w)
return (dx, dw)
def run_shift():
B, C, H, W = 16, 32, 32, 32
R, S, F = 3, 3, 32
B, C, H, W = 1, 16, 8, 8
R, S, F = 3, 3, 16
a = tf.placeholder(tf.float32, shape=[C, H, W, B])
b = tf.placeholder(tf.float32, shape=[C, F])
shift_h = tf.zeros(C, tf.int32)
shift_w = tf.zeros(C, tf.int32)
hshift_h = np.zeros(C, np.int32)
hshift_w = np.zeros(C, np.int32)
#hshift_h = np.random.randint(-R//2, R//2 + 1, size=C, dtype=np.int32)
#hshift_w = np.random.randint(-S//2, R//2 + 1, size=C, dtype=np.int32)
hshift_h = 0*np.ones(C, dtype=np.int32)
hshift_w = 0*np.ones(C, dtype=np.int32)
c = module.shift_conv(a, b, shift_h=tf.make_tensor_proto(hshift_h), shift_w=tf.make_tensor_proto(hshift_w))
# Reference
ha = np.random.rand(C, H, W, B)
hb = np.random.rand(C, F)
# Run
ha = np.ones((C, H, W, B), dtype=np.int32)
hb = np.ones((C, F), dtype=np.int32)
sess = tf.InteractiveSession()
grads = tf.test.compute_gradient([a, b], [(C, H, W, B), (C, F)], c, (C, H, W, B),
extra_feed_dict={a: ha, b: hb})
dx_t, dx_n = grads[0]
dw_t, dw_n = grads[1]
print(dw_t)
print(dw_n)
#print(np.max(dw_t - dw_n))
#print(np.max(dx_t - dx_n))
np.savetxt('theoretical.dat', dw_t, fmt='%4.2f')
np.savetxt('numerical.dat', dw_n, fmt='%4.2f')
# Run
sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {a: ha,
b: hb})[0]
#print(result)
run_shift()

111
examples/python/tensorflow/shift.cpp
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@@ -19,6 +19,15 @@
using namespace tensorflow;
using GPUDevice = Eigen::GpuDevice;
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*,
triton::dnn::shift::type, bool> shift_key_t;
static std::map<CUstream, std::unique_ptr<triton::driver::stream>> m_stream;
static std::map<shift_key_t, std::unique_ptr<triton::jit>> m_jit;
static std::map<shift_key_t, std::unique_ptr<triton::dnn::shift>> m_config;
template<triton::dnn::shift::type OP>
class ShiftConvOp : public OpKernel {
public:
@@ -78,15 +87,27 @@ public:
// shapes
int64_t C, H, W, B, F;
FillShapes(context, C, H, W, B, F, tf_a, tf_b);
int64_t D = 1, T = 1;
bool has_bias = false;
// shift configuration
int32_t* shift_h_data = h_shift_h_.flat<int32_t>().data();
int32_t* shift_w_data = h_shift_w_.flat<int32_t>().data();
std::vector<int32_t> shift_h(shift_h_data, shift_h_data + C);
std::vector<int32_t> shift_w(shift_w_data, shift_w_data + C);
triton::dnn::shift shift(B, C, 1, H, W, 1, R_, S_, F, shift_h, shift_w, "fp32", "fp32", OP, false);
shift_key_t key = {B, C, 1, H, W, 1, R_, S_, F, shift_h_data, shift_w_data, OP, has_bias};
// create configuration
triton::dnn::shift* shift;
if(m_config.find(key) == m_config.end())
shift = m_config.emplace(key, new triton::dnn::shift(
B, C, D, H, W, T, R_, S_, F,
shift_h, shift_w, "fp32", "fp32", OP, has_bias))
.first->second.get();
else
shift = m_config.at(key).get();
// shapes for c
std::vector<int64> c_shapes;
for(int32_t x: shift.c_shapes())
for(int32_t x: shift->c_shapes())
c_shapes.push_back(x);
TensorShape out_shapes(c_shapes);
Tensor* tf_c = nullptr;
@@ -94,38 +115,58 @@ public:
// return early if possible
if (out_shapes.num_elements() == 0)
return;
// initialize default compute device
triton::jit jit(ctx);
// matrix multiplication parameters
triton::driver::cu_buffer da(ctx, (CUdeviceptr)tf_a.flat<float>().data(), false);
triton::driver::cu_buffer db(ctx, (CUdeviceptr)tf_b.flat<float>().data(), false);
triton::driver::cu_buffer dc(ctx, (CUdeviceptr)tf_c->flat<float>().data(), false);
// benchmark a given matrix multiplication kernel
auto benchmark = [&](triton::driver::kernel* kernel,
triton::jit::launch_information info) {
// launch info
unsigned TM = info.global_range_size[0];
unsigned TN = info.global_range_size[1];
unsigned nthreads = info.num_threads;
shift.init(stream, (triton::driver::cu_module*)kernel->module());
shift.enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads);
stream->synchronize();
double ts = triton::tools::bench([&](){ shift.enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads); },
[&](){ stream->synchronize(); }, ctx->device());
return shift.get_nflops() / ts * 1e-3;
};
std::ostringstream oss;
shift.src(oss);
std::string src = oss.str();
triton::jit::tune_res_t best = jit.autotune("shift", src.c_str(), benchmark);
// get JIT
triton::jit* jit;
bool autotune = false;
if(m_jit.find(key) == m_jit.end()) {
jit = m_jit.emplace(key, new triton::jit(ctx)).first->second.get();
std::ostringstream oss;
shift->src(oss);
std::string src = oss.str();
auto benchmark = [&](triton::driver::kernel* kernel,
triton::jit::launch_information info) {
// launch info
unsigned TM = info.global_range_size[0];
unsigned TN = info.global_range_size[1];
unsigned nthreads = info.num_threads;
shift->init(stream, (triton::driver::cu_module*)kernel->module());
shift->enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads);
stream->synchronize();
double ts = triton::tools::bench([&](){ shift->enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads); },
[&](){ stream->synchronize(); }, ctx->device());
return shift->get_nflops() / ts * 1e-3;
};
// auto-tune and save result
if(autotune) {
triton::jit::tune_res_t best = jit->autotune("shift", src.c_str(), benchmark);
jit->add_module("shift", src.c_str(), best.params);
}
else {
jit->add_module("shift", src.c_str(), jit->get_valid("shift", src.c_str()));
}
triton::driver::kernel* kernel = jit->get_function("shift");
shift->init(stream, (triton::driver::cu_module*)kernel->module());
}
else
jit = m_jit.at(key).get();
// Run
triton::driver::kernel* kernel = jit->get_function("shift");
triton::jit::launch_information info = jit->get_launch_info("shift");
// launch info
unsigned TM = info.global_range_size[0];
unsigned TN = info.global_range_size[1];
unsigned nthreads = info.num_threads;
// enqueue
shift->enqueue(stream, kernel, &da, &db, &dc, TM, TN, nthreads);
}
private:
Tensor h_shift_h_;
Tensor h_shift_w_;
// triton::driver::buffer* d_shift_h_;
// triton::driver::buffer* d_shift_w_;
int R_;
int S_;
};
@@ -136,5 +177,21 @@ REGISTER_OP("ShiftConv")
.Input("b: float32")
.Attr("shift_h: tensor")
.Attr("shift_w: tensor")
.Output("c: float32")
;
.Output("c: float32");
REGISTER_KERNEL_BUILDER(Name("ShiftConvDx").Device(DEVICE_GPU), ShiftConvOp<triton::dnn::shift::BPROP>);
REGISTER_OP("ShiftConvDx")
.Input("a: float32")
.Input("b: float32")
.Attr("shift_h: tensor")
.Attr("shift_w: tensor")
.Output("c: float32");
REGISTER_KERNEL_BUILDER(Name("ShiftConvDw").Device(DEVICE_GPU), ShiftConvOp<triton::dnn::shift::WGRAD>);
REGISTER_OP("ShiftConvDw")
.Input("a: float32")
.Input("b: float32")
.Attr("shift_h: tensor")
.Attr("shift_w: tensor")
.Output("c: float32");

1
include/triton/runtime/jit.h
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@@ -103,6 +103,7 @@ private:
public:
jit(driver::context* context);
~jit();
std::vector<unsigned> get_valid(const char *name, const char *src);
tune_res_t autotune(const char* name, const char* src, benchmark_t benchmark);
void add_module(ir::module &module, const std::vector<unsigned>& params = {});
void add_module(const char* name, const char* src, const std::vector<unsigned>& params = {});

55
lib/dnn/shift.cpp
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@@ -70,16 +70,26 @@ shift::shift(int B, int C,
}
void shift::build_deltas() {
// compute offset
auto offset = [&](unsigned c) {
return c*ld_a_[0] + shift_h_[c]*ld_a_[1] + shift_w_[c]*ld_a_[2];
};
h_deltas_.resize(MAX_C_);
// populate look-up table
for(unsigned c = 0; c < TK_; c++)
h_deltas_[c] = offset(c);
for(unsigned c = 0; c < C_; c++)
h_deltas_[TK_ + c] = offset(c + TK_) - offset(c);
if(ty_ == FPROP){
// compute offset
auto offset = [&](unsigned c) {
return c*ld_a_[0] + shift_h_[c]*ld_a_[1] + shift_w_[c]*ld_a_[2];
};
// populate look-up table
for(unsigned c = 0; c < TK_; c++)
h_deltas_[c] = offset(c);
for(unsigned c = 0; c < C_; c++)
h_deltas_[TK_ + c] = offset(c + TK_) - offset(c);
}
if(ty_ == BPROP){
for(unsigned c = 0; c < C_; c++)
h_deltas_[c] = shift_h_[c]*ld_c_[1] + shift_w_[c]*ld_c_[2];
}
if(ty_ == WGRAD){
for(unsigned c = 0; c < C_; c++)
h_deltas_[c] = shift_h_[c]*ld_b_[1] + shift_w_[c]*ld_b_[2];
}
}
size_t shift::a_size(){
@@ -102,7 +112,7 @@ std::vector<int32_t> shift::c_shapes(){
}
size_t shift::get_nflops() {
return 2. * M_ * N_ * K_;
return 2.*M_*N_*K_;
}
@@ -114,15 +124,13 @@ void shift::init(driver::stream *stream, driver::cu_module *module) {
void shift::enqueue(driver::stream *stream, driver::kernel *kernel,
driver::buffer *a, driver::buffer *b, driver::buffer *c,
size_t TM, size_t TN, size_t nthreads) {
if(ty_ == WGRAD)
std::swap(a, b);
kernel->setArg(0, a);
kernel->setArg(1, b);
kernel->setArg(2, c);
kernel->setArg(3, M_);
kernel->setArg(4, N_);
kernel->setArg(5, K_);
kernel->setArg(6, B_*AH_*AW_);
kernel->setArg(6, M_);
kernel->setArg(7, N_);
kernel->setArg(8, B_);
kernel->setArg(9, AH_);
@@ -177,7 +185,7 @@ void shift(restrict read_only align(16) )" << a_ty_ << R"( *a,
restrict read_only align(16) )" << b_ty_ << R"( *b,
fp32 *c,
int32 M, int32 N, int32 K,
multiple_of(4) int32 lda, multiple_of(4) int32 ldb,
int32 lda, int32 ldb,
int32 ABS, int32 AH, int32 AW, int32 AR, int32 AS) {
int32 rxa[TM] = get_global_range[TM](0);
int32 ryb[TN] = get_global_range[TN](1);
@@ -203,11 +211,13 @@ if(ty_ == FPROP){
}
if(ty_ == WGRAD){
os << R"(
int32 shift[TK, TN] = 0;)";
__constant__ int32* pd[TN] = delta + ryb;
int32 d[TN] = *pd;
int32 shift[TK, TN] = d[newaxis, :];)";
}
os << R"(
)" << a_ty_ << "* pa[" << AS << "] = a + rxa" << bca1 << " + " << rka << bca0 << lda0 << R"(;
)" << b_ty_ << "* pb[" << BS << "] = b + ryb" << bcb1 << " + " << rkb << bcb0 << ldb0 << R"(;
)" << a_ty_ << "* pa[" << AS << "] = a + rxa" << bca1 << lda1 << " + " << rka << bca0 << lda0 << R"(;
)" << b_ty_ << "* pb[" << BS << "] = b + ryb" << bcb1 << ldb1 << " + " << rkb << bcb0 << ldb0 << R"(;
)" << a_ty_ << " a[" << AS << R"(] = *pa;
)" << b_ty_ << " b[" << BS << R"(] = *pb;
for(int32 k = K; k > 0; k = k - TK){
@@ -239,7 +249,7 @@ if(ty_ == WGRAD){
int1 maskw[TK] = (rbw >= pad_w) && (rbw < (AW - pad_w));
int1 mask[TK, TN] = maskh[:, newaxis] && maskw[:, newaxis];
int32 inc[TK, TN] = mask ? 0 : shift;
pb = pb + TK;
pb = pb + TK)" << ldb0 << R"(;
)" << b_ty_ << R"(* pbb[TK, TN] = pb + inc;
@checkb b = *pbb;)";
}
@@ -259,14 +269,15 @@ else{
if(ty_ == BPROP){
os << R"(
int32 rcwhc[TM] = rxc / ABS;
int32 rcw[TM] = rcwhc % AW;
int32 rcw[TM] = (rcwhc % AW);
int32 rchc[TM] = rcwhc / AW;
int32 rch[TM] = rchc % AH;
int32 rch[TM] = (rchc % AH);
int1 maskh[TM] = (rch >= pad_h) && (rch < (AH - pad_h));
int1 maskw[TM] = (rcw >= pad_w) && (rcw < (AW - pad_w));
int1 interior[TM, TN] = maskh[:, newaxis] && maskw[:, newaxis];
fp32* shiftpc[TM, TN] = pc + 0;
pc = interior ? shiftpc : pc;
__constant__ int32* pd[TN] = delta + ryc;
fp32* shift_pc[TM, TN] = pc + (*pd)[newaxis, :];
pc = interior ? shift_pc : pc;
@checkc __atomic_add(pc, C);
)";
}

2
lib/driver/module.cpp
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@@ -255,7 +255,7 @@ std::string cu_module::compile_llvm_module(llvm::Module* module) {
cu_module::cu_module(driver::context * context, llvm::Module* ll_module): cu_module(context, compile_llvm_module(ll_module)) { }
cu_module::cu_module(driver::context * context, std::string const & source) : module(context, CUmodule(), true), source_(source){
// std::cout << source << sd::endl;
// std::cout << source << std::endl;
cu_context::context_switcher ctx_switch(*context);
// JIT compile source-code
CUjit_option opt[] = {CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES, CU_JIT_ERROR_LOG_BUFFER};

40
lib/runtime/jit.cpp
View File

@@ -96,6 +96,46 @@ jit::jit(driver::context *context): driver_context_(context),
jit::~jit(){ }
std::vector<unsigned> jit::get_valid(const char *name, const char *src) {
// find metaparameters
auto ptt_module = make_triton_module(name, src);
ir::module &tt_module = *ptt_module;
// set parameters
passes_wrapper passes(target_.get());
passes.target_independent(tt_module);
passes.tune.run(tt_module);
auto mps = passes.tune.get_params(tt_module);
// create parameter ranges
std::vector<std::vector<unsigned>> ranges;
for(ir::metaparameter *mp: mps)
ranges.push_back(mp->get_space());
// iterate over parameters
std::vector<unsigned> result;
loop_nest<unsigned>(ranges, [&](const std::vector<unsigned> params){
if(!result.empty())
return;
std::map<ir::value*, std::vector<std::string>> errors;
unsigned i = 0;
for(ir::metaparameter *mp: mps)
mp->set_value(params[i++]);
passes.target_independent(tt_module);
passes.tune.init(tt_module);
passes.tune.check_constraints(errors);
// for(auto e: errors)
// for(auto x: e.second)
// std::cout << x << std::endl;
// std::cout << "-----" << std::endl;
if(!errors.empty())
return;
result = params;
});
if(result.empty())
throw std::runtime_error("couldn't find valid parameters");
return result;
}
jit::tune_res_t jit::autotune(const char *name, const char *src, benchmark_t benchmark) {
// find metaparameters
auto ptt_module = make_triton_module(name, src);