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dnnl.cc
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dnnl.cc
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*!
* \file src/runtime/contrib/dnnl/dnnl.cc
* \brief TVM compatible wrappers for dnnl kernels.
*/
#include <assert.h>
#include <stdlib.h>
#include <string.h>
#include <algorithm>
#include <iostream>
#include <numeric>
#include <string>
#include <vector>
#include "dnnl_kernel.h"
namespace tvm {
namespace runtime {
namespace contrib {
using namespace dnnl;
typedef struct {
void** data;
} DnnlPackedArgs;
// Read from memory, write to handle
inline void read_from_dnnl_memory(void* handle, const memory& mem) {
size_t bytes = mem.get_desc().get_size();
uint8_t* src = static_cast<uint8_t*>(mem.get_data_handle());
std::copy(src, src + bytes, reinterpret_cast<uint8_t*>(handle));
}
void dnnl_conv2d_common(float* data, float* weights, float* bias, float* out, int p_N_, int p_C_,
int p_H_, int p_W_, int p_O_, int p_G_, int p_Ph_, int p_Pw_, int p_Kh_,
int p_Kw_, int p_Sh_, int p_Sw_, primitive_attr attr) {
using tag = memory::format_tag;
using dt = memory::data_type;
engine eng(engine::kind::cpu, 0);
stream s(eng);
memory::dims conv2d_src_tz = {p_N_, p_C_, p_H_, p_W_};
memory::dims conv2d_weights_tz = {p_O_, p_C_, p_Kh_, p_Kw_};
if (p_G_ > 1) conv2d_weights_tz = {p_G_, 1, p_C_ / p_G_, p_Kh_, p_Kw_};
memory::dims conv2d_bias_tz = {p_O_};
memory::dims conv2d_dst_tz = {p_N_, p_O_, (p_H_ - p_Kh_ + 2 * p_Ph_ + p_Sh_) / p_Sh_,
(p_W_ - p_Kw_ + 2 * p_Pw_ + p_Sw_) / p_Sw_};
memory::dims conv2d_strides = {p_Sh_, p_Sw_};
memory::dims conv2d_padding = {p_Ph_, p_Pw_};
auto user_src_memory = memory({{conv2d_src_tz}, dt::f32, tag::nchw}, eng, data);
auto user_weights_memory =
memory({{conv2d_weights_tz}, dt::f32, (p_G_ > 1) ? tag::goihw : tag::oihw}, eng, weights);
auto conv2d_user_bias_memory = memory({{conv2d_bias_tz}, dt::f32, tag::x}, eng, bias);
auto conv2d_src_md = memory::desc({conv2d_src_tz}, dt::f32, tag::any);
auto conv2d_bias_md = memory::desc({conv2d_bias_tz}, dt::f32, tag::any);
auto conv2d_weights_md = memory::desc({conv2d_weights_tz}, dt::f32, tag::any);
auto conv2d_dst_md = memory::desc({conv2d_dst_tz}, dt::f32, tag::nchw);
auto conv2d_desc = convolution_forward::desc(
prop_kind::forward_inference, algorithm::convolution_direct, conv2d_src_md, conv2d_weights_md,
conv2d_bias_md, conv2d_dst_md, conv2d_strides, conv2d_padding, conv2d_padding);
auto conv2d_prim_desc = convolution_forward::primitive_desc(conv2d_desc, attr, eng);
auto conv2d_src_memory = user_src_memory;
auto conv2d_weights_memory = user_weights_memory;
auto conv2d_dst_memory = memory(conv2d_prim_desc.dst_desc(), eng);
auto conv = convolution_forward(conv2d_prim_desc);
conv.execute(s, {{DNNL_ARG_SRC, conv2d_src_memory},
{DNNL_ARG_WEIGHTS, conv2d_weights_memory},
{DNNL_ARG_BIAS, conv2d_user_bias_memory},
{DNNL_ARG_DST, conv2d_dst_memory}});
s.wait();
read_from_dnnl_memory(out, conv2d_dst_memory);
}
extern "C" void dnnl_conv2d(float* data, float* weights, float* out, int p_N_, int p_C_, int p_H_,
int p_W_, int p_O_, int p_G_, int p_Ph_, int p_Pw_, int p_Kh_,
int p_Kw_, int p_Sh_, int p_Sw_) {
primitive_attr attr;
std::vector<float> bias(p_O_, 0);
return dnnl_conv2d_common(data, weights, bias.data(), out, p_N_, p_C_, p_H_, p_W_, p_O_, p_G_,
p_Ph_, p_Pw_, p_Kh_, p_Kw_, p_Sh_, p_Sw_, attr);
}
primitive_attr create_attr_with_relu_post_op() {
post_ops ops;
ops.append_eltwise(1.f, algorithm::eltwise_relu, 0.f, 0.f);
primitive_attr attr;
attr.set_post_ops(ops);
return attr;
}
extern "C" void dnnl_fused_conv2d_relu(float* data, float* weights, float* out, int p_N_, int p_C_,
int p_H_, int p_W_, int p_O_, int p_G_, int p_Ph_, int p_Pw_,
int p_Kh_, int p_Kw_, int p_Sh_, int p_Sw_) {
std::vector<float> bias(p_O_, 0);
return dnnl_conv2d_common(data, weights, bias.data(), out, p_N_, p_C_, p_H_, p_W_, p_O_, p_G_,
p_Ph_, p_Pw_, p_Kh_, p_Kw_, p_Sh_, p_Sw_,
create_attr_with_relu_post_op());
}
extern "C" void dnnl_fused_conv2d_bias_relu(float* data, float* weights, float* bias, float* out,
int p_N_, int p_C_, int p_H_, int p_W_, int p_O_,
int p_G_, int p_Ph_, int p_Pw_, int p_Kh_, int p_Kw_,
int p_Sh_, int p_Sw_) {
return dnnl_conv2d_common(data, weights, bias, out, p_N_, p_C_, p_H_, p_W_, p_O_, p_G_, p_Ph_,
p_Pw_, p_Kh_, p_Kw_, p_Sh_, p_Sw_, create_attr_with_relu_post_op());
}
extern "C" void dnnl_dense(float* data, float* weight, float* out, int p_B_, int p_I_, int p_O_) {
using tag = memory::format_tag;
using dt = memory::data_type;
engine eng(engine::kind::cpu, 0);
stream s(eng);
memory::dims data_tz = {p_B_, p_I_};
memory::dims weight_tz = {p_O_, p_I_};
memory::dims bias_tz = {p_O_};
memory::dims dst_tz = {p_B_, p_O_};
auto data_md = memory::desc{{data_tz}, dt::f32, tag::nc};
auto weight_md = memory::desc({{weight_tz}, dt::f32, tag::nc});
auto bias_md = memory::desc({{bias_tz}, dt::f32, tag::x});
auto dst_md = memory::desc({{dst_tz}, dt::f32, tag::nc});
std::vector<float> bias(p_O_, 0);
auto data_memory = memory(data_md, eng, data);
auto weight_memory = memory(weight_md, eng, weight);
auto bias_memory = memory(bias_md, eng, bias.data());
auto dst_memory = memory(dst_md, eng);
auto dense_desc = inner_product_forward::desc(prop_kind::forward_inference, data_md, weight_md,
bias_md, dst_md);
auto dense_prim_desc = inner_product_forward::primitive_desc(dense_desc, eng);
assert(dst_md == dense_prim_desc.dst_desc());
auto dense = inner_product_forward(dense_prim_desc);
dense.execute(s, {{DNNL_ARG_SRC, data_memory},
{DNNL_ARG_WEIGHTS, weight_memory},
{DNNL_ARG_BIAS, bias_memory},
{DNNL_ARG_DST, dst_memory}});
s.wait();
read_from_dnnl_memory(out, dst_memory);
}
extern "C" void dnnl_relu(float* data, float* out, int p_N_, int p_C_, int p_H_, int p_W_) {
using tag = memory::format_tag;
using dt = memory::data_type;
engine eng(engine::kind::cpu, 0);
stream s(eng);
memory::dims data_tz = {p_N_, p_C_, p_H_, p_W_};
auto data_md = memory::desc{{data_tz}, dt::f32, tag::nchw};
auto data_memory = memory(data_md, eng, data);
auto dst_memory = memory(data_md, eng);
auto relu_desc =
eltwise_forward::desc(prop_kind::forward_inference, algorithm::eltwise_relu, data_md, 0);
auto relu_prim_desc = eltwise_forward::primitive_desc(relu_desc, eng);
assert(data_md == relu_prim_desc.dst_desc());
auto relu = eltwise_forward(relu_prim_desc);
relu.execute(s, {{DNNL_ARG_SRC, data_memory}, {DNNL_ARG_DST, dst_memory}});
s.wait();
read_from_dnnl_memory(out, dst_memory);
}
extern "C" void dnnl_bn(float* data, float* gamma, float* beta, float* mean, float* variance,
float* out, float* new_mean, float* new_variance, int p_N_, int p_C_,
int p_H_, int p_W_, int p_E_) {
using tag = memory::format_tag;
using dt = memory::data_type;
engine eng(engine::kind::cpu, 0);
stream s(eng);
memory::dims data_tz = {p_N_, p_C_, p_H_, p_W_};
auto data_md = memory::desc{{data_tz}, dt::f32, tag::nchw};
auto data_memory = memory(data_md, eng, data);
auto dst_memory = memory(data_md, eng);
auto bn_desc = batch_normalization_forward::desc(
prop_kind::forward_inference, data_md, p_E_,
normalization_flags::use_global_stats | normalization_flags::use_scale_shift);
auto bn_prim_desc = batch_normalization_forward::primitive_desc(bn_desc, eng);
assert(data_md == bn_prim_desc.dst_desc());
float* weight = reinterpret_cast<float*>(malloc(sizeof(float) * 2 * p_C_));
memcpy(weight, gamma, sizeof(float) * p_C_);
memcpy(weight + p_C_, beta, sizeof(float) * p_C_);
auto weight_memory = memory(bn_prim_desc.weights_desc(), eng, weight);
auto mean_memory = memory(bn_prim_desc.mean_desc(), eng, mean);
auto variance_memory = memory(bn_prim_desc.variance_desc(), eng, variance);
auto bn = batch_normalization_forward(bn_prim_desc);
bn.execute(s, {{DNNL_ARG_SRC, data_memory},
{DNNL_ARG_DST, dst_memory},
{DNNL_ARG_SCALE_SHIFT, weight_memory},
{DNNL_ARG_MEAN, mean_memory},
{DNNL_ARG_VARIANCE, variance_memory}});
s.wait();
read_from_dnnl_memory(out, dst_memory);
free(weight);
}
extern "C" void dnnl_add(float* data, float* weight, float* out, int p_N_, int p_C_, int p_H_,
int p_W_) {
using tag = memory::format_tag;
using dt = memory::data_type;
engine eng(engine::kind::cpu, 0);
stream s(eng);
memory::dims data_tz = {p_N_, p_C_, p_H_, p_W_};
auto data_md = memory::desc{{data_tz}, dt::f32, tag::nchw};
auto weight_md = memory::desc({{data_tz}, dt::f32, tag::nchw});
auto dst_md = memory::desc({{data_tz}, dt::f32, tag::nchw});
auto data_memory = memory(data_md, eng, data);
auto weight_memory = memory(weight_md, eng, weight);
auto dst_memory = memory(dst_md, eng);
auto add_desc = binary::desc(algorithm::binary_add, data_md, weight_md, dst_md);
auto add_prim_desc = binary::primitive_desc(add_desc, eng);
assert(dst_md == add_prim_desc.dst_desc());
auto add = binary(add_prim_desc);
add.execute(
s,
{{DNNL_ARG_SRC_0, data_memory}, {DNNL_ARG_SRC_1, weight_memory}, {DNNL_ARG_DST, dst_memory}});
s.wait();
read_from_dnnl_memory(out, dst_memory);
}
} // namespace contrib
} // namespace runtime
} // namespace tvm