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acl_gemm_convolution.cpp
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/*******************************************************************************
* Copyright 2020-2024 Arm Ltd. and affiliates
*
* Licensed 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.
*******************************************************************************/
#include "acl_gemm_convolution.hpp"
#include "acl_convolution_utils.hpp"
#include "common/memory_tracking.hpp"
namespace dnnl {
namespace impl {
namespace cpu {
namespace aarch64 {
namespace {
// Keys are anonymous. So deduce the type automagically.
using conv_key_t = decltype(memory_tracking::names::key_gemm_tmp_buffer);
// Map: [slot , key]
// These correspond to the information provided by Op::workspace, which
// specifies a unique numbered slot (not necessarily in continous ascending
// order) for each key.
const std::map<int, conv_key_t> gemm_conv_keys
= {{0, conv_key_t::key_gemm_asm_tmp_buffer},
{1, conv_key_t::key_gemm_pretranspose_b},
{2, conv_key_t::key_gemm_pretranspose},
{3, conv_key_t::key_gemm_interleaved_lhs},
{4, conv_key_t::key_gemm_pretransposed_rhs},
{5, conv_key_t::key_gemm_transposed_1xwrhs},
{6, conv_key_t::key_gemm_tmp_buffer},
{7, conv_key_t::key_gemm_mm_result_s32},
{8, conv_key_t::key_gemm_mm_signed_a},
{9, conv_key_t::key_gemm_mm_signed_output},
{10, conv_key_t::key_conv_gemm_col},
{11, conv_key_t::key_conv_permuted_weights},
{12, conv_key_t::key_gemm_output}};
} // namespace
template <data_type_t src_t, data_type_t wei_t, data_type_t dst_t,
data_type_t bia_t>
status_t acl_gemm_convolution_fwd_t<src_t, wei_t, dst_t, bia_t>::pd_t::init(
engine_t *engine) {
using namespace data_type;
using smask_t = primitive_attr_t::skip_mask_t;
bool ok = is_fwd() && set_default_alg_kind(alg_kind::convolution_direct)
&& expect_data_types(src_t, wei_t, bia_t, dst_t, undef)
&& !has_zero_dim_memory()
&& attr()->has_default_values(
smask_t::post_ops | smask_t::fpmath_mode, dst_t);
if (!ok) return status::unimplemented;
if (weights_md_.ndims != 4) return status::unimplemented;
// General Compute Library checks, memory tags are also set there
CHECK(acl_convolution_utils::acl_init_conf(
acp_, src_md_, weights_md_, dst_md_, bias_md_, *desc(), *attr()));
// Validate convolution manually to check for return status
ACL_CHECK_VALID(Op::validate(&acp_.src_tensor_info, &acp_.wei_tensor_info,
acp_.with_bias ? &acp_.bia_tensor_info : nullptr,
&acp_.dst_tensor_info, acp_.padstride_info, acp_.weights_info,
acp_.dilation_info, acp_.act_info, acp_.fast_math));
Op conv;
conv.configure(&acp_.src_tensor_info, &acp_.wei_tensor_info,
acp_.with_bias ? &acp_.bia_tensor_info : nullptr,
&acp_.dst_tensor_info, acp_.padstride_info, acp_.weights_info,
acp_.dilation_info, acp_.act_info, acp_.fast_math);
auto scratchpad = scratchpad_registry().registrar();
const auto mem_req = conv.workspace();
return init_scratchpad(conv, scratchpad, gemm_conv_keys, engine, post_ops,
attr_.post_ops_, acp_.act_info, acp_.use_dst_acc_for_sum, dst_md_);
}
template <data_type_t src_t, data_type_t wei_t, data_type_t dst_t,
data_type_t bia_t>
status_t acl_gemm_convolution_fwd_t<src_t, wei_t, dst_t, bia_t>::init(
engine_t *engine) {
// commented due to hot fix solution for stateless API which should be replaced soon.
// auto acp_ = pd()->acp_;
// acl_obj_->conv.configure(&acp_.src_tensor_info, &acp_.wei_tensor_info,
// acp_.with_bias ? &acp_.bia_tensor_info : nullptr,
// &acp_.dst_tensor_info, acp_.padstride_info, acp_.weights_info,
// acp_.dilation_info, acp_.act_info, acp_.fast_math);
// acl_obj_->aux_mem_req = acl_obj_->conv.workspace();
return status::success;
}
template <data_type_t src_type, data_type_t wei_type, data_type_t dst_type,
data_type_t bia_type>
std::unique_ptr<acl_obj_t<typename acl_gemm_convolution_fwd_t<src_type,
wei_type, dst_type, bia_type>::Op>>
acl_gemm_convolution_fwd_t<src_type, wei_type, dst_type,
bia_type>::reinitialize_acl_obj() const {
auto acp_ = pd()->acp_;
std::unique_ptr<acl_obj_t<Op>> acl_obj = std::make_unique<acl_obj_t<Op>>();
acl_obj->conv.configure(&acp_.src_tensor_info, &acp_.wei_tensor_info,
acp_.with_bias ? &acp_.bia_tensor_info : nullptr,
&acp_.dst_tensor_info, acp_.padstride_info, acp_.weights_info,
acp_.dilation_info, acp_.act_info, acp_.fast_math);
acl_obj->aux_mem_req = acl_obj->conv.workspace();
return acl_obj;
}
template <data_type_t src_t, data_type_t wei_t, data_type_t dst_t,
data_type_t bia_t>
status_t
acl_gemm_convolution_fwd_t<src_t, wei_t, dst_t, bia_t>::execute_forward(
const exec_ctx_t &ctx) const {
// Temporary hotfix: We're using a local acl_obj instance in this method
// instead of the class member acl_obj_. This hotfix is to bypass persistent aux mem requirements but is not the ideal solution.
// It should be refactored or removed in the future when a more permanent fix is implemented.
auto acl_obj = reinitialize_acl_obj();
return execute_forward_conv_acl<acl_obj_t<Op>, pd_t, src_data_t, wei_data_t,
dst_data_t, bia_data_t>(ctx, acl_obj.get(), pd(), gemm_conv_keys);
}
using namespace data_type;
template struct acl_gemm_convolution_fwd_t<f32>;
template struct acl_gemm_convolution_fwd_t<f16>;
template struct acl_gemm_convolution_fwd_t<s8, s8, s8, s32>;
} // namespace aarch64
} // namespace cpu
} // namespace impl
} // namespace dnnl