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[examples] added tensorflow dense convolution templates

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This commit is contained in:
Philippe Tillet
2019-06-26 11:39:22 -07:00
parent 25e9a10917
commit f1a8972267
4 changed files with 156 additions and 24 deletions
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2
examples/python/tensorflow/CMakeLists.txt
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@@ -5,7 +5,7 @@ if(${TensorFlow_FOUND})
include_directories("${CUDA_HOME}/include")
link_directories(${TF_LIB})
add_definitions(-D_GLIBCXX_USE_CXX11_ABI=${TF_ABI})
add_library(tf_blocksparse SHARED dot.cpp)
add_library(tf_blocksparse SHARED dot.cpp dense_conv)
target_link_libraries(tf_blocksparse tensorflow_framework triton)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/run.py
${CMAKE_CURRENT_BINARY_DIR}/run.py

117
examples/python/tensorflow/dense_conv.cpp Normal file
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@@ -0,0 +1,117 @@
#include <iostream>
#include "triton/driver/buffer.h"
#include "triton/driver/backend.h"
#include "triton/driver/stream.h"
#include "triton/runtime/jit.h"
#include "triton/tools/bench.hpp"
#include "triton/dnn/gemm.h"
#include "triton/dnn/conv.h"
#define EIGEN_USE_GPU
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/util/cuda_kernel_helper.h"
#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"
#include "tensorflow/core/framework/common_shape_fns.h"
using namespace tensorflow;
using GPUDevice = Eigen::GpuDevice;
//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,
// int32_t stride_d, int32_t stride_h, int32_t stride_w,
// int32_t pad_d, int32_t pad_h, int32_t pad_w,
// triton::dnn::conv::type ty,
// torch::Tensor torcha, torch::Tensor torchb, torch::Tensor torchbias,
// bool autotune = false
// ) {
//}
class DenseConvOp : public OpKernel {
public:
explicit DenseConvOp(OpKernelConstruction* context) : OpKernel(context) {
}
void Compute(OpKernelContext* context){
// get device/stream
GPUDevice device = context->eigen_device<GPUDevice>();
triton::driver::cu_stream sstream(device.stream(), false);
triton::driver::context* ctx = sstream.context();
triton::driver::stream* stream = &sstream;
// get inputs
const Tensor& tfa = context->input(0);
const Tensor& tfb = context->input(1);
// get shapes
int32_t B = tfa.dim_size(0);
int32_t Ca = tfa.dim_size(1);
int32_t D = 1;
int32_t H = tfa.dim_size(2);
int32_t W = tfa.dim_size(3);
int32_t Cb = tfb.dim_size(0);
int32_t T = 1;
int32_t R = tfb.dim_size(1);
int32_t S = tfb.dim_size(2);
int32_t NF = tfb.dim_size(3);
assert(Ca == Cb);
int32_t C = Ca;
int32_t stride_d = 1, stride_h = 1, stride_w = 1;
int32_t pad_d = 0, pad_h = 0, pad_w = 0;
bool has_bias = false;
// get conv configuration
triton::dnn::conv configuration(B, C, D, H, W, T, R, S, NF,
stride_d, stride_h, stride_w,
pad_d, pad_h, pad_w,
1, 1, 1,
triton::dnn::conv::FPROP, has_bias);
// Bind memory
triton::driver::cu_buffer a(ctx, (CUdeviceptr)tfa.flat<float>().data(), false);
triton::driver::cu_buffer b(ctx, (CUdeviceptr)tfb.flat<float>().data(), false);
// triton::driver::cu_buffer cubias(ctx, (CUdeviceptr)torchbias.storage().data(), false);
// triton::driver::buffer* bias = has_bias ? &cubias : nullptr;
triton::driver::buffer* bias = nullptr;
// allocate output
auto c_shapes = configuration.c_shapes();
Tensor* tfc = nullptr;
TensorShape out_shape({c_shapes[0], c_shapes[1], c_shapes[2], c_shapes[3]});
OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &tfc));
triton::driver::cu_buffer c(ctx, (CUdeviceptr)tfc->flat<float>().data(), false);
// benchmark a given convolution kernel
triton::jit jit(ctx);
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;
};
std::ostringstream oss;
configuration.src(oss);
std::string src = oss.str();
triton::jit::tune_res_t best = jit.autotune("conv", src.c_str(), benchmark);
jit.add_module("conv", src.c_str(), best.params);
}
};
REGISTER_KERNEL_BUILDER(Name("DenseConv").Device(DEVICE_GPU), DenseConvOp);
REGISTER_OP("DenseConv")
.Input("a: float32")
.Input("b: float32")
.Output("c: float32")
;

7
examples/python/tensorflow/dot.cpp
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@@ -19,9 +19,9 @@
using namespace tensorflow;
using GPUDevice = Eigen::GpuDevice;
class BlockSparseGemmOp : public OpKernel {
class DotOp : public OpKernel {
public:
explicit BlockSparseGemmOp(OpKernelConstruction* context) : OpKernel(context) {
explicit DotOp(OpKernelConstruction* context) : OpKernel(context) {
}
void Compute(OpKernelContext* context){
@@ -52,7 +52,6 @@ class BlockSparseGemmOp : public OpKernel {
triton::driver::cu_buffer db(ctx, (CUdeviceptr)b.flat<Eigen::half>().data(), false);
triton::driver::cu_buffer dc(ctx, (CUdeviceptr)c->flat<float>().data(), false);
triton::driver::cu_buffer dlocks(ctx, (CUdeviceptr)locks.flat<int32_t>().data(), false);
stream->synchronize();
// benchmark a given matrix multiplication kernel
auto benchmark = [&](triton::driver::kernel* kernel,
triton::jit::launch_information info) {
@@ -85,7 +84,7 @@ class BlockSparseGemmOp : public OpKernel {
private:
};
REGISTER_KERNEL_BUILDER(Name("Dot").Device(DEVICE_GPU), BlockSparseGemmOp);
REGISTER_KERNEL_BUILDER(Name("Dot").Device(DEVICE_GPU), DotOp);
REGISTER_OP("Dot")
.Input("a: float16")
.Input("b: float16")

54
examples/python/tensorflow/run.py
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@@ -6,24 +6,40 @@ 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'))
M, N, K = 128,128,128
a = tf.placeholder(tf.float16, shape=[M, K])
b = tf.placeholder(tf.float16, shape=[N, K])
locks = tf.placeholder(tf.int32, shape=[4096])
# c = tf.matmul(a, b, transpose_a=True)
c = module.dot(a, b, locks)
def run_dot():
M, N, K = 128,128,128
a = tf.placeholder(tf.float16, shape=[M, K])
b = tf.placeholder(tf.float16, shape=[N, K])
locks = tf.placeholder(tf.int32, shape=[4096])
# c = tf.matmul(a, b, transpose_a=True)
c = module.dot(a, b, locks)
# Reference
ha = np.random.rand(M, K).astype(np.float16)
hb = np.random.rand(N, K).astype(np.float16)
# Run
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {locks: np.zeros(4096),
a: ha,
b: hb})[0]
# Test
hresult = np.dot(ha.T, hb).T
dif = np.abs(result - hresult)
print("dif: %f" % np.max(dif))
# Reference
ha = np.random.rand(M, K).astype(np.float16)
hb = np.random.rand(N, K).astype(np.float16)
def run_conv():
BS, C, H, W = 16, 32, 32, 32
R, S, NF = 3, 3, 32
a = tf.placeholder(tf.float32, shape=[BS, C, H, W])
b = tf.placeholder(tf.float32, shape=[C, R, S, NF])
c = module.dense_conv(a, b)
# Reference
ha = np.random.rand(BS, C, H, W)
hb = np.random.rand(C, R, S, NF)
# Run
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {a: ha,
b: hb})[0]
# Run
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
result = sess.run([c], feed_dict = {locks: np.zeros(4096),
a: ha,
b: hb})[0]
hresult = np.dot(ha.T, hb).T
dif = np.abs(result - hresult)
print("dif: %f" % np.max(dif))
run_conv()