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conv_2d.cc
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conv_2d.cc
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#include "flexflow/ops/conv_2d.h"
#include "flexflow/layer.h"
#include "flexflow/model.h"
#include "flexflow/ops/kernels/conv_2d_kernels.h"
#include "flexflow/utils/hash_utils.h"
#include "legion/legion_utilities.h"
#include "mpark/variant.hpp"
namespace FlexFlow {
// declare Legion names
using Legion::ArgumentMap;
using Legion::Context;
using Legion::coord_t;
using Legion::Domain;
using Legion::FutureMap;
using Legion::IndexLauncher;
using Legion::InlineLauncher;
using Legion::PhysicalRegion;
using Legion::Predicate;
using Legion::Rect;
using Legion::RegionRequirement;
using Legion::Runtime;
using Legion::Task;
using Legion::TaskArgument;
using Legion::TaskLauncher;
using namespace FlexFlow::Kernels::Conv2D;
Tensor FFModel::conv2d(const Tensor input,
int outChannels,
int kernelH,
int kernelW,
int strideH,
int strideW,
int paddingH,
int paddingW,
ActiMode activation,
int groups,
bool use_bias,
Layer const *shared_op,
Initializer *kernel_initializer,
Initializer *bias_initializer,
char const *name) {
assert(input->num_dims == 4); /*NCHW*/
Layer *conv = new Layer(this,
OP_CONV2D,
DT_FLOAT,
name,
1 /*inputs*/,
use_bias ? 2 : 1 /*weights*/,
1 /*outputs*/,
input);
{
int numdims = 4;
int dims[MAX_TENSOR_DIM];
dims[3] = input->dims[3];
dims[2] = outChannels;
dims[1] = 1 + (input->dims[1] + 2 * paddingH - kernelH) / strideH;
dims[0] = 1 + (input->dims[0] + 2 * paddingW - kernelW) / strideW;
conv->outputs[0] = create_tensor_legion_ordering(
numdims, dims, DT_FLOAT, conv, 0, true /*create_grad*/);
}
{
int dims[4] = {kernelW, kernelH, input->dims[2], outChannels};
conv->weights[0] = create_weight_legion_ordering(4,
dims,
DT_FLOAT,
conv,
true /*create_grad*/,
kernel_initializer,
CHOSEN_SYNC_TYPE);
}
if (use_bias) {
int dims[1] = {outChannels};
conv->weights[1] = create_weight_legion_ordering(1,
dims,
DT_FLOAT,
conv,
true /*create_grad*/,
bias_initializer,
CHOSEN_SYNC_TYPE);
}
conv->add_int_property("out_channels", outChannels);
conv->add_int_property("kernel_h", kernelH);
conv->add_int_property("kernel_w", kernelW);
conv->add_int_property("stride_h", strideH);
conv->add_int_property("stride_w", strideW);
conv->add_int_property("padding_h", paddingH);
conv->add_int_property("padding_w", paddingW);
conv->add_int_property("activation", activation);
conv->add_int_property("groups", groups);
conv->add_int_property("use_bias", use_bias);
conv->add_initializer("kernel", kernel_initializer);
conv->add_initializer("bias", bias_initializer);
layers.push_back(conv);
return conv->outputs[0];
}
Op *Conv2D::create_operator_from_layer(
FFModel &model,
Layer const *layer,
std::vector<ParallelTensor> const &inputs) {
long long value;
layer->get_int_property("out_channels", value);
int out_channels = value;
layer->get_int_property("kernel_h", value);
int kernelH = value;
layer->get_int_property("kernel_w", value);
int kernelW = value;
layer->get_int_property("stride_h", value);
int strideH = value;
layer->get_int_property("stride_w", value);
int strideW = value;
layer->get_int_property("padding_h", value);
int paddingH = value;
layer->get_int_property("padding_w", value);
int paddingW = value;
layer->get_int_property("activation", value);
ActiMode activation = (ActiMode)value;
layer->get_int_property("groups", value);
int groups = value;
layer->get_int_property("use_bias", value);
bool use_bias = value;
Initializer *kernel_initializer, *bias_initializer;
layer->get_initializer("kernel", kernel_initializer);
layer->get_initializer("bias", bias_initializer);
return new Conv2D(model,
layer->layer_guid,
inputs[0],
out_channels,
kernelH,
kernelW,
strideH,
strideW,
paddingH,
paddingW,
activation,
groups,
use_bias,
false, // allocate_weights
layer->name);
}
Conv2DParams Conv2D::get_params() const {
Conv2DParams params;
params.layer_guid = this->layer_guid;
params.out_channels = this->out_channels;
params.kernel_h = this->kernel_h;
params.kernel_w = this->kernel_w;
params.stride_h = this->stride_h;
params.stride_w = this->stride_w;
params.padding_h = this->padding_h;
params.padding_w = this->padding_w;
params.activation = this->activation;
params.groups = this->groups;
params.use_bias = this->use_bias;
return params;
}
// size_t Conv2DParams::get_hash(const ParallelTensor input) const {
// size_t hash = input->get_owner_independent_hash();
// hash_combine(hash, this->layer_guid.id);
// hash_combine(hash, this->out_channels);
// hash_combine(hash, this->kernel_h);
// hash_combine(hash, this->kernel_w);
// hash_combine(hash, this->stride_h);
// hash_combine(hash, this->stride_w);
// hash_combine(hash, this->padding_h);
// hash_combine(hash, this->padding_w);
// hash_combine(hash, this->activation);
// hash_combine(hash, this->groups);
// hash_combine(hash, this->use_bias);
// return hash;
// }
// size_t Conv2D::get_params_hash() const {
// return this->get_params().get_hash(this->inputs[0]);
// }
using PCG::Node;
bool operator==(Conv2DParams const &lhs, Conv2DParams const &rhs) {
return lhs.layer_guid == rhs.layer_guid && lhs.kernel_h == rhs.kernel_h &&
lhs.kernel_w == rhs.kernel_w && lhs.stride_h == rhs.stride_h &&
lhs.stride_w == rhs.stride_w && lhs.padding_h == rhs.padding_h &&
lhs.padding_w == rhs.padding_w && lhs.groups == rhs.groups &&
lhs.activation == rhs.activation && lhs.use_bias == rhs.use_bias;
}
void Conv2DParams::mark_replica_dims(
ParallelTensorShape const &input,
ParallelDim output_dims[MAX_TENSOR_DIM],
ParallelDim kernel_dims[MAX_TENSOR_DIM],
ParallelDim bias_dims[MAX_TENSOR_DIM]) const {
if (output_dims != nullptr) {
output_dims[Conv2DOutput::REPLICA].is_replica_dim = true;
}
if (kernel_dims != nullptr) {
kernel_dims[Conv2DOutput::REPLICA].is_replica_dim = true;
}
if (bias_dims != nullptr) {
bias_dims[Conv2DBias::REPLICA_1].is_replica_dim = true;
bias_dims[Conv2DBias::REPLICA_2].is_replica_dim = true;
bias_dims[Conv2DBias::REPLICA_3].is_replica_dim = true;
bias_dims[Conv2DBias::REPLICA_4].is_replica_dim = true;
}
}
int Conv2DParams::output_size(ParallelTensorShape const &input,
ParallelDim output_dims[MAX_TENSOR_DIM]) const {
int input_w = input.dims[Conv2DInput::WIDTH].size;
int input_h = input.dims[Conv2DInput::HEIGHT].size;
output_dims[Conv2DOutput::SAMPLE].size = input.dims[Conv2DInput::SAMPLE].size;
output_dims[Conv2DOutput::CHANNEL].size = out_channels;
output_dims[Conv2DOutput::HEIGHT].size =
1 + (input_h + 2 * padding_h - kernel_h) / stride_h;
output_dims[Conv2DOutput::WIDTH].size =
1 + (input_w + 2 * padding_w - kernel_w) / stride_w;
return input.num_dims;
};
int Conv2DParams::kernel_size(ParallelTensorShape const &input,
ParallelDim kernel_dims[MAX_TENSOR_DIM]) const {
kernel_dims[Conv2DKernel::CHANNEL_OUT].size = this->out_channels;
kernel_dims[Conv2DKernel::CHANNEL_IN].size =
input.dims[Conv2DInput::CHANNEL].size / this->groups;
kernel_dims[Conv2DKernel::HEIGHT].size =
this->kernel_h * input.dims[Conv2DInput::HEIGHT].degree;
kernel_dims[Conv2DKernel::WIDTH].size =
this->kernel_w * input.dims[Conv2DInput::WIDTH].degree;
return Conv2DKernel::NUMDIM;
}
int Conv2DParams::bias_size(ParallelTensorShape const &input,
ParallelDim bias_dims[MAX_TENSOR_DIM]) const {
bias_dims[Conv2DBias::CHANNEL].size = this->out_channels;
return Conv2DBias::NUMDIM;
};
void Conv2DParams::solve_dims(ParallelTensorShape const &input,
ParallelDim output_dims[MAX_TENSOR_DIM],
int *output_ndims,
ParallelDim kernel_dims[MAX_TENSOR_DIM],
int *kernel_ndims,
ParallelDim bias_dims[MAX_TENSOR_DIM],
int *bias_ndims) const {
assert((output_dims == nullptr) == (output_ndims == nullptr));
assert((kernel_dims == nullptr) == (kernel_ndims == nullptr));
assert((bias_dims == nullptr) == (bias_ndims == nullptr));
std::vector<ParallelDimMappingRecord> mapping;
Conv2D::construct_mappings(mapping, this->use_bias);
this->mark_replica_dims(input, output_dims, kernel_dims, bias_dims);
std::vector<ParallelDim *> output_dim_sets;
if (output_dims != nullptr) {
output_dim_sets.push_back(output_dims);
}
std::vector<ParallelDim *> weight_dim_sets;
if (kernel_dims != nullptr) {
weight_dim_sets.push_back(kernel_dims);
}
if (bias_dims != nullptr && this->use_bias) {
weight_dim_sets.push_back(bias_dims);
}
solve_parallel_dim_mappings(
mapping, {input.dims}, weight_dim_sets, output_dim_sets);
if (output_dims != nullptr) {
*output_ndims = this->output_size(input, output_dims);
}
if (kernel_dims != nullptr) {
*kernel_ndims = this->kernel_size(input, kernel_dims);
}
if (bias_dims != nullptr && this->use_bias) {
*bias_ndims = this->bias_size(input, bias_dims);
}
}
/*static*/
void Conv2D::construct_mappings(std::vector<ParallelDimMappingRecord> &out,
bool use_bias) {
Conv2D::construct_output_mappings(out);
Conv2D::construct_weight_mappings(out, use_bias);
}
/*static*/
void Conv2D::construct_output_mappings(
std::vector<ParallelDimMappingRecord> &out) {
Op::construct_output_parallel_dims(
out,
{{Conv2DInput::CHANNEL,
MappingOperation::REPLICATE,
Conv2DOutput::REPLICA},
{Conv2DInput::SAMPLE, MappingOperation::PARTITION, Conv2DOutput::SAMPLE},
{Conv2DInput::REPLICA,
MappingOperation::PARTITION,
Conv2DOutput::CHANNEL},
{Conv2DInput::HEIGHT, MappingOperation::PARTITION, Conv2DOutput::HEIGHT},
{Conv2DInput::WIDTH, MappingOperation::PARTITION, Conv2DOutput::WIDTH}});
}
/*static*/
void Conv2D::construct_weight_mappings(
std::vector<ParallelDimMappingRecord> &out, bool use_bias) {
Op::construct_weight_parallel_dims(
out,
{
{Conv2DInput::REPLICA,
MappingOperation::PARTITION,
Conv2DKernel::CHANNEL_OUT},
{Conv2DInput::SAMPLE,
MappingOperation::REPLICATE,
Conv2DKernel::REPLICA},
{Conv2DInput::CHANNEL,
MappingOperation::PARTITION,
Conv2DKernel::CHANNEL_IN},
{Conv2DInput::HEIGHT,
MappingOperation::REPLICATE,
Conv2DKernel::HEIGHT}, // Kernel::{HEIGHT, WEIGHT} would both work
// here
{Conv2DInput::WIDTH,
MappingOperation::REPLICATE,
Conv2DKernel::WIDTH}, // same as above
},
Conv2DInput::INDEX,
Conv2DKernel::INDEX);
if (use_bias) {
Op::construct_weight_parallel_dims(
out,
{{Conv2DInput::REPLICA, Conv2DBias::REPLICA_1},
{Conv2DInput::SAMPLE, Conv2DBias::REPLICA_2},
{Conv2DInput::CHANNEL, Conv2DBias::CHANNEL},
{Conv2DInput::HEIGHT, Conv2DBias::REPLICA_3},
{Conv2DInput::WIDTH, Conv2DBias::REPLICA_4}},
Conv2DInput::INDEX,
Conv2DBias::INDEX);
}
}
Conv2D::Conv2D(FFModel &model,
Conv2D const &other,
const ParallelTensor input,
bool allocate_weights)
: Conv2D(model,
other.layer_guid,
input,
other.out_channels,
other.kernel_h,
other.kernel_w,
other.stride_h,
other.stride_w,
other.padding_h,
other.padding_w,
other.activation,
other.groups,
other.use_bias,
allocate_weights,
other.name) {}
Conv2D::Conv2D(FFModel &model,
Conv2DParams const ¶ms,
ParallelTensor const input,
char const *name,
bool allocate_weights)
: Conv2D(model,
params.layer_guid,
input,
params.out_channels,
params.kernel_h,
params.kernel_w,
params.stride_h,
params.stride_w,
params.padding_h,
params.padding_w,
params.activation,
params.groups,
params.use_bias,
allocate_weights,
name) {}
bool Conv2DParams::is_valid(ParallelTensorShape const &input) const {
ParallelTensorShape output_shape, kernel_shape, bias_shape;
this->solve_dims(input,
output_shape.dims,
&output_shape.num_dims,
kernel_shape.dims,
&kernel_shape.num_dims,
bias_shape.dims,
&bias_shape.num_dims);
bool is_valid = true;
is_valid &= input.is_valid();
is_valid &= output_shape.is_valid();
is_valid &= kernel_shape.is_valid();
if (use_bias) {
is_valid &= bias_shape.is_valid();
}
// TODO FIXME: Currently disable parallelizing the height and width dimension
if (input.dims[0].degree > 1 || input.dims[1].degree > 1) {
return false;
}
return is_valid;
}
Conv2D::Conv2D(FFModel &model,
LayerID const &_layer_guid,
const ParallelTensor input,
int outChannels,
int kernelH,
int kernelW,
int strideH,
int strideW,
int paddingH,
int paddingW,
ActiMode activation,
int groups,
bool use_bias,
bool allocate_weights,
char const *name)
: Op(model,
OP_CONV2D,
DT_FLOAT,
name,
1 /*inputs*/,
use_bias ? 2 : 1 /*weights*/,
allocate_weights,
1 /*outputs*/,
input),
in_channels(input->dims[Conv2DInput::CHANNEL].size /
input->dims[Conv2DInput::CHANNEL].degree),
out_channels(outChannels), kernel_h(kernelH), kernel_w(kernelW),
stride_h(strideH), stride_w(strideW), padding_h(paddingH),
padding_w(paddingW), activation(activation), groups(groups),
use_bias(use_bias) {
// overwrite layer_guid
layer_guid = _layer_guid;
assert(input->num_dims == Conv2DInput::NUMDIM);
assert(this->stride_h > 0);
assert(this->stride_w > 0);
ParallelDim output_dims[MAX_TENSOR_DIM], kernel_dims[MAX_TENSOR_DIM],
bias_dims[MAX_TENSOR_DIM];
int output_ndims, kernel_ndims, bias_ndims;
this->construct_mappings(*this->parallel_dims_mapping, this->use_bias);
this->get_params().solve_dims(this->inputs[0]->get_shape(),
output_dims,
&output_ndims,
kernel_dims,
&kernel_ndims,
bias_dims,
&bias_ndims);
if (allocate_weights) {
Initializer *kernel_initializer = new GlorotUniform(std::rand() /*seed*/);
weights[Conv2DKernel::INDEX] =
model.create_parallel_weight_legion_ordering(kernel_ndims,
kernel_dims,
DT_FLOAT,
NULL /*owner_op*/,
true /*create_grad*/,
kernel_initializer,
CHOSEN_SYNC_TYPE);
if (use_bias) {
Initializer *bias_initializer = new ZeroInitializer();
weights[Conv2DBias::INDEX] =
model.create_parallel_weight_legion_ordering(bias_ndims,
bias_dims,
DT_FLOAT,
NULL /*owner_op*/,
true /*create_grad*/,
bias_initializer,
CHOSEN_SYNC_TYPE);
}
}
outputs[0] = model.create_parallel_tensor_legion_ordering(
output_ndims, output_dims, DT_FLOAT, this);
assert(check_output_input_weight_parallel_dims(allocate_weights));
}
void Conv2D::init(FFModel const &ff) {
assert(check_output_input_weight_same_parallel_is());
parallel_is = outputs[0]->parallel_is;
ArgumentMap argmap;
Context ctx = ff.config.lg_ctx;
Runtime *runtime = ff.config.lg_hlr;
set_argumentmap_for_init(ff, argmap);
IndexLauncher launcher(CONV2D_INIT_TASK_ID,
parallel_is,
TaskArgument(this, sizeof(Conv2D)),
argmap,
Predicate::TRUE_PRED,
false /*must*/,
0 /*mapper_id*/,
outputs[0]->machine_view.hash());
launcher.add_region_requirement(RegionRequirement(inputs[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
inputs[0]->region));
launcher.add_field(0, FID_DATA);
launcher.add_region_requirement(RegionRequirement(outputs[0]->part,
0 /*projection id*/,
WRITE_ONLY,
EXCLUSIVE,
outputs[0]->region));
launcher.add_field(1, FID_DATA);
launcher.add_region_requirement(RegionRequirement(weights[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
weights[0]->region));
launcher.add_field(2, FID_DATA);
// launcher.add_region_requirement(
// RegionRequirement(weights[1]->part, 0/*projection id*/,
// READ_ONLY, EXCLUSIVE, weights[1]->region));
// launcher.add_field(3, FID_DATA);
launcher.add_region_requirement(RegionRequirement(weights[0]->part_grad,
0 /*projection id*/,
WRITE_ONLY,
EXCLUSIVE,
weights[0]->region_grad));
launcher.add_field(3, FID_DATA);
// launcher.add_region_requirement(
// RegionRequirement(inputs[0]->part_grad, 0/*projection id*/,
// WRITE_ONLY, EXCLUSIVE, inputs[0]->region_grad));
// launcher.add_field(4, FID_DATA);
FutureMap fm = runtime->execute_index_space(ctx, launcher);
fm.wait_all_results();
set_opmeta_from_futuremap(ff, fm);
}
/*
regions[0]: input
regions[1]: output
regions[2](I): filter
regions[3](I): bias
regions[4](O): filter_grad
regions[5](O): input_grad
*/
OpMeta *Conv2D::init_task(Task const *task,
std::vector<PhysicalRegion> const ®ions,
Context ctx,
Runtime *runtime) {
assert(regions.size() == 4);
assert(task->regions.size() == 4);
Conv2D const *conv = (Conv2D *)task->args;
FFHandler handle = *((FFHandler const *)task->local_args);
TensorAccessorR<float, Conv2DInput::NUMDIM> acc_input(
regions[0], task->regions[0], FID_DATA, ctx, runtime);
TensorAccessorW<float, Conv2DOutput::NUMDIM> acc_output(regions[1],
task->regions[1],
FID_DATA,
ctx,
runtime,
false /*readOutput*/);
TensorAccessorR<float, Conv2DKernel::NUMDIM> acc_kernel(
regions[2], task->regions[2], FID_DATA, ctx, runtime);
TensorAccessorR<float, Conv2DBias::NUMDIM> acc_bias(
regions[3], task->regions[3], FID_DATA, ctx, runtime);
TensorAccessorW<float, Conv2DKernel::NUMDIM> acc_kernel_grad(
regions[3],
task->regions[3],
FID_DATA,
ctx,
runtime,
false /*readOutput*/);
// TensorAccessorW<float, 4> acc_input_grad(
// regions[4], task->regions[4], FID_DATA, ctx, runtime,
// false/*readOutput*/);
Conv2DMeta *m = new Conv2DMeta(handle);
m->relu = conv->activation == AC_MODE_RELU;
m->use_bias = conv->use_bias;
m->profiling = conv->profiling;
m->trainableInputs[0] = conv->trainableInputs[0];
std::strcpy(m->op_name, conv->name);
int input_w = acc_input.rect.hi[0] - acc_input.rect.lo[0] + 1;
int input_h = acc_input.rect.hi[1] - acc_input.rect.lo[1] + 1;
int input_c = acc_input.rect.hi[2] - acc_input.rect.lo[2] + 1;
int input_n = acc_input.rect.hi[3] - acc_input.rect.lo[3] + 1;
int output_w = acc_output.rect.hi[0] - acc_output.rect.lo[0] + 1;
int output_h = acc_output.rect.hi[1] - acc_output.rect.lo[1] + 1;
int output_c = acc_output.rect.hi[2] - acc_output.rect.lo[2] + 1;
int output_n = acc_output.rect.hi[3] - acc_output.rect.lo[3] + 1;
printf("init conv (input): n(%d) c(%d) h(%d) w(%d)\n",
input_n,
input_c,
input_h,
input_w);
printf("init conv (output): n(%d) c(%d) h(%d) w(%d)\n",
output_n,
output_c,
output_h,
output_w);
// printf("convDim: padding(%d %d) stride(%d %d)\n", conv->padding_h,
// conv->padding_w, conv->stride_h, conv->stride_w);
int pad_h =
((output_h - 1) * conv->stride_h + conv->kernel_h - input_h + 1) / 2;
int pad_w =
((output_w - 1) * conv->stride_w + conv->kernel_w - input_w + 1) / 2;
if (pad_h != conv->padding_h) {
printf("Warning: changing conv_padding_h to satisfy output_h size\n");
}
if (pad_w != conv->padding_w) {
printf("Warning: changing conv_padding_w to satisfy output_w size\n");
}
init_kernel(m,
input_w,
input_h,
input_c,
input_n,
output_w,
output_h,
output_c,
output_n,
conv->kernel_h,
conv->kernel_w,
conv->groups,
conv->stride_h,
conv->stride_w,
pad_h,
pad_w,
acc_input.ptr,
acc_output.ptr,
acc_kernel.ptr,
acc_kernel_grad.ptr);
return m;
}
void Conv2D::forward(FFModel const &ff) {
ArgumentMap argmap;
Context ctx = ff.config.lg_ctx;
Runtime *runtime = ff.config.lg_hlr;
set_argumentmap_for_forward(ff, argmap);
IndexLauncher launcher(CONV2D_FWD_TASK_ID,
parallel_is,
TaskArgument(NULL, 0),
argmap,
Predicate::TRUE_PRED,
false /*must*/,
0 /*mapper_id*/,
outputs[0]->machine_view.hash());
launcher.add_region_requirement(RegionRequirement(inputs[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
inputs[0]->region));
launcher.add_field(0, FID_DATA);
launcher.add_region_requirement(RegionRequirement(outputs[0]->part,
0 /*projection id*/,
WRITE_ONLY,
EXCLUSIVE,
outputs[0]->region));
launcher.add_field(1, FID_DATA);
launcher.add_region_requirement(RegionRequirement(weights[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
weights[0]->region));
launcher.add_field(2, FID_DATA);
if (use_bias) {
launcher.add_region_requirement(RegionRequirement(weights[1]->region,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
weights[1]->region));
launcher.add_field(3, FID_DATA);
}
runtime->execute_index_space(ctx, launcher);
}
/*
regions[0](I): input
regions[1](O): output
regions[2](I): filter
regions[3](I): bias
*/
void Conv2D::forward_task(Task const *task,
std::vector<PhysicalRegion> const ®ions,
Context ctx,
Runtime *runtime) {
// Conv2D* conv = (Conv2D*) task->args;
Conv2DMeta const *m = *((Conv2DMeta **)task->local_args);
assert(regions.size() == (3 + static_cast<size_t>(m->use_bias)));
assert(task->regions.size() == (3 + static_cast<size_t>(m->use_bias)));
TensorAccessorR<float, Conv2DInput::NUMDIM> acc_input(
regions[0], task->regions[0], FID_DATA, ctx, runtime);
TensorAccessorW<float, Conv2DOutput::NUMDIM> acc_output(regions[1],
task->regions[1],
FID_DATA,
ctx,
runtime,
false /*readOutput*/);
TensorAccessorR<float, Conv2DKernel::NUMDIM> acc_kernel(
regions[2], task->regions[2], FID_DATA, ctx, runtime);
float const *acc_bias_ptr = NULL;
if (m->use_bias) {
TensorAccessorR<float, Conv2DBias::NUMDIM> acc_bias(
regions[3], task->regions[3], FID_DATA, ctx, runtime);
acc_bias_ptr = acc_bias.ptr;
}
forward_kernel_wrapper(
m, acc_input.ptr, acc_output.ptr, acc_kernel.ptr, acc_bias_ptr);
}
void Conv2D::backward(FFModel const &ff) {
ArgumentMap argmap;
Context ctx = ff.config.lg_ctx;
Runtime *runtime = ff.config.lg_hlr;
set_argumentmap_for_backward(ff, argmap);
IndexLauncher launcher(CONV2D_BWD_TASK_ID,
parallel_is,
TaskArgument(NULL, 0),
argmap,
Predicate::TRUE_PRED,
false /*must*/,
0 /*mapper_id*/,
outputs[0]->machine_view.hash());
int rid = 0;
// regions[0](I): input
launcher.add_region_requirement(RegionRequirement(inputs[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
inputs[0]->region));
launcher.add_field(rid++, FID_DATA);
// regions[1](I/O): input_grad
if (trainableInputs[0]) {
launcher.add_region_requirement(RegionRequirement(inputs[0]->part_grad,
0 /*projection id*/,
READ_WRITE,
EXCLUSIVE,
inputs[0]->region_grad));
launcher.add_field(rid++, FID_DATA);
}
// regions[2](I): output
launcher.add_region_requirement(RegionRequirement(outputs[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
outputs[0]->region));
launcher.add_field(rid++, FID_DATA);
// regions[3](I/O): output_grad
launcher.add_region_requirement(RegionRequirement(outputs[0]->part_grad,
0 /*projection id*/,
READ_WRITE,
EXCLUSIVE,
outputs[0]->region_grad));
launcher.add_field(rid++, FID_DATA);
// regions[4](I): filter
launcher.add_region_requirement(RegionRequirement(weights[0]->part,
0 /*projection id*/,
READ_ONLY,
EXCLUSIVE,
weights[0]->region));
launcher.add_field(rid++, FID_DATA);
// regions[5](I/O): filter_grad
launcher.add_region_requirement(RegionRequirement(weights[0]->part_grad,
0 /*projection id*/,
READ_WRITE,
EXCLUSIVE,
weights[0]->region_grad));
launcher.add_field(rid++, FID_DATA);
if (use_bias) {
// regions[6](I/O): bias_grad
launcher.add_region_requirement(RegionRequirement(weights[1]->part_grad,
0 /*projection id*/,
READ_WRITE,
EXCLUSIVE,
weights[1]->region_grad));
launcher.add_field(rid++, FID_DATA);
}
FutureMap fm = runtime->execute_index_space(ctx, launcher);
}
/*
region(I): input
region(I/O): input_grad (if trainableInputs[0])
region(I): output
region(I/O): output_grad
region(I): filter
region(I/O): filter_grad
region(I/O): bias_grad (if use_bias)
*/
void Conv2D::backward_task(Task const *task,
std::vector<PhysicalRegion> const ®ions,
Context ctx,
Runtime *runtime) {
// Conv2D* conv = (Conv2D*) task->args;
Conv2DMeta const *m = *((Conv2DMeta **)task->local_args);
assert(regions.size() == (5 + static_cast<size_t>(m->trainableInputs[0]) +
static_cast<size_t>(m->use_bias)));
assert(task->regions.size() ==
(5 + static_cast<size_t>(m->trainableInputs[0]) +
static_cast<size_t>(m->use_bias)));
size_t rid = 0;
TensorAccessorR<float, Conv2DInput::NUMDIM> acc_input(
regions[rid], task->regions[rid], FID_DATA, ctx, runtime);
rid++;
float *acc_input_grad_ptr = NULL;
if (m->trainableInputs[0]) {
TensorAccessorW<float, Conv2DInput::NUMDIM> acc_input_grad(
regions[rid],
task->regions[rid],
FID_DATA,
ctx,
runtime,
true /*readOutput*/);
acc_input_grad_ptr = acc_input_grad.ptr;
rid++;
}
TensorAccessorR<float, Conv2DOutput::NUMDIM> acc_output(
regions[rid], task->regions[rid], FID_DATA, ctx, runtime);
rid++;
TensorAccessorW<float, Conv2DOutput::NUMDIM> acc_output_grad(
regions[rid],
task->regions[rid],
FID_DATA,
ctx,
runtime,
true /*readOutput*/);
rid++;
TensorAccessorR<float, Conv2DKernel::NUMDIM> acc_kernel(
regions[rid], task->regions[rid], FID_DATA, ctx, runtime);
rid++;
TensorAccessorW<float, Conv2DKernel::NUMDIM> acc_kernel_grad(
regions[rid],
task->regions[rid],
FID_DATA,
ctx,
runtime,
true /*readOutput*/);
rid++;
float *acc_bias_grad_ptr = NULL;
if (m->use_bias) {
TensorAccessorW<float, Conv2DBias::NUMDIM> acc_bias_grad(
regions[rid],
task->regions[rid],
FID_DATA,
ctx,
runtime,
true /*readOutput*/);
acc_bias_grad_ptr = static_cast<float *>(acc_bias_grad.ptr);
rid++;
}
assert(rid == regions.size());
backward_kernel_wrapper(m,
acc_input.ptr,
acc_input_grad_ptr,
acc_output.ptr,
acc_output_grad.ptr,
acc_kernel.ptr,
acc_kernel_grad.ptr,
acc_bias_grad_ptr);
}
void Conv2D::print_layer(FFModel const &ff) {
printf("conv2d layer\n");
Context ctx = ff.config.lg_ctx;
Runtime *runtime = ff.config.lg_hlr;
#if 0
TaskLauncher launcher(CONV2D_PRINT_TASK_ID, TaskArgument(NULL, 0));
launcher.add_region_requirement(
RegionRequirement(kernel->region, READ_ONLY, EXCLUSIVE, kernel->region));
launcher.add_field(0, FID_DATA);
launcher.add_region_requirement(
RegionRequirement(bias->region, READ_ONLY, EXCLUSIVE, bias->region));
launcher.add_field(1, FID_DATA);
Future fu = runtime->execute_task(ctx, launcher);
fu.wait();
#else
RegionRequirement kernel_req(
weights[0]->region, READ_WRITE, EXCLUSIVE, weights[0]->region);
kernel_req.add_field(FID_DATA);
InlineLauncher kernel_launcher(kernel_req);
PhysicalRegion kernel_region = runtime->map_region(ctx, kernel_launcher);
kernel_region.wait_until_valid();
/*
RegionRequirement kernel_grad_req(kernel->region_grad, READ_WRITE,
EXCLUSIVE, kernel->region_grad); kernel_grad_req.add_field(FID_DATA);
InlineLauncher kernel_grad_launcher(kernel_grad_req);
PhysicalRegion kernel_grad_region = runtime->map_region(ctx,
kernel_grad_launcher); kernel_grad_region.wait_until_valid();
*/
RegionRequirement bias_req(
weights[1]->region, READ_WRITE, EXCLUSIVE, weights[1]->region);
bias_req.add_field(FID_DATA);
InlineLauncher bias_launcher(bias_req);
PhysicalRegion bias_region = runtime->map_region(ctx, bias_launcher);
bias_region.wait_until_valid();
/*
RegionRequirement bias_grad_req(bias->region_grad, READ_WRITE, EXCLUSIVE,
bias->region_grad); bias_grad_req.add_field(FID_DATA); InlineLauncher
bias_grad_launcher(bias_grad_req); PhysicalRegion bias_grad_region =
runtime->map_region(ctx, bias_grad_launcher);
bias_grad_region.wait_until_valid();
*/
TensorAccessorW<float, Conv2DKernel::NUMDIM> acc_kernel(
kernel_region, kernel_req, FID_DATA, ctx, runtime, true);
// const AccessorRW<float, 1> acc_kernel_grad(kernel_grad_region, FID_DATA);
TensorAccessorW<float, Conv2DBias::NUMDIM> acc_bias(
bias_region, bias_req, FID_DATA, ctx, runtime, true);
// const AccessorRW<float, 1> acc_bias_grad(bias_grad_region, FID_DATA);
float const *kernel_ptr = acc_kernel.ptr;
// float *kernel_grad_ptr = acc_kernel_grad.ptr;
float const *bias_ptr = acc_bias.ptr;
// float *bias_grad_ptr = acc_bias_grad.ptr;
size_t kernel_size = acc_kernel.rect.volume();
int kernel_dim1 = acc_kernel.rect.hi[0] - acc_kernel.rect.lo[0] + 1;
int kernel_dim2 = acc_kernel.rect.hi[1] - acc_kernel.rect.lo[1] + 1;
int kernel_dim3 = acc_kernel.rect.hi[2] - acc_kernel.rect.lo[2] + 1;
int kernel_dim4 = acc_kernel.rect.hi[3] - acc_kernel.rect.lo[3] + 1;
// size_t kernel_grad_size = rect_kernel_grad.volume();
size_t bias_size = acc_bias.rect.volume();
// size_t bias_grad_size = rect_bias_grad.volume();
printf("kernel, %p, %zu, [%d, %d, %d, %d]\n",
kernel_ptr,
kernel_size,
kernel_dim1,
kernel_dim2,
kernel_dim3,
kernel_dim4);
// printf("kernel_grad, %d\n", kernel_grad_size);
printf("bias, %p, %zu\n", bias_ptr, bias_size);
// printf("bias_grad, %d\n", bias_grad_size);
for (size_t i = 0; i < bias_size; i++) {
printf("%f ", bias_ptr[i]);
}
printf("\n");
/*
for (int i = 0; i < bias_grad_size; i++) {
printf("%f ", bias_grad_ptr);
bias_grad_ptr ++;
}
printf("\n");*/
for (size_t i = 0; i < kernel_size; i++) {
printf("%f ", kernel_ptr[i]);
}
printf("\n");
/*
for (int i = 0; i < kernel_grad_size; i++) {
printf("%f ", kernel_grad_ptr);
kernel_grad_ptr ++;
}
printf("\n");
*/
runtime->unmap_region(ctx, kernel_region);
// runtime->unmap_region(ctx, kernel_grad_region);
runtime->unmap_region(ctx, bias_region);
// runtime->unmap_region(ctx, bias_grad_region);
#endif
}
bool Conv2D::estimate_sync_cost(Simulator *sim,
MachineView const &view,
CostMetrics &cost_metrics) const {
ParallelDim kernel_dims[MAX_TENSOR_DIM], bias_dims[MAX_TENSOR_DIM];
int kernel_ndims, bias_ndims;
this->get_params().solve_dims(this->inputs[0]->get_shape(),
nullptr,
nullptr,
kernel_dims,
&kernel_ndims,
bias_dims,
&bias_ndims);