/******************************************************************************* * Copyright 2018 Intel Corporation * * 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 #include "oneapi/dnnl/dnnl.h" #include "opdesc.hpp" #include "primitive_desc_iface.hpp" #include "c_types_map.hpp" #include "type_helpers.hpp" #include "utils.hpp" using namespace dnnl::impl; using namespace dnnl::impl::utils; using namespace dnnl::impl::status; using namespace dnnl::impl::prop_kind; using namespace dnnl::impl::alg_kind; using namespace dnnl::impl::types; #define VCHECK_DECONV(cond, msg, ...) \ VCONDCHECK(primitive, create, check, deconvolution, (cond), \ status::invalid_arguments, msg, ##__VA_ARGS__) #define VCHECK_DECONV_UNIMPL(cond, msg, ...) \ VCONDCHECK(primitive, create, check, deconvolution, (cond), \ status::unimplemented, msg, ##__VA_ARGS__) namespace { status_t deconv_desc_init(deconvolution_desc_t *deconv_desc, prop_kind_t prop_kind, alg_kind_t alg_kind, const memory_desc_t *src_desc, const memory_desc_t *weights_desc, const memory_desc_t *bias_desc, const memory_desc_t *dst_desc, const dims_t strides, const dims_t dilates, const dims_t padding_l, const dims_t padding_r) { VCHECK_DECONV(!any_null(deconv_desc, src_desc, weights_desc, dst_desc, strides, padding_l), VERBOSE_NULL_ARG); VCHECK_DECONV( one_of(alg_kind, deconvolution_direct, deconvolution_winograd), VERBOSE_BAD_ALGORITHM); VCHECK_DECONV(!any_memory_desc_host_scalar( src_desc, weights_desc, bias_desc, dst_desc), VERBOSE_UNSUPPORTED_FORMAT_KIND); if (padding_r == nullptr) padding_r = padding_l; auto dd = deconvolution_desc_t(); dd.primitive_kind = primitive_kind::deconvolution; dd.prop_kind = prop_kind; dd.alg_kind = alg_kind; dd.use_inversion = false; // Must be always `false` for deconv. dd.diff_src_desc = dd.src_desc = zero_md(); dd.diff_dst_desc = dd.dst_desc = zero_md(); dd.diff_weights_desc = dd.weights_desc = zero_md(); dd.diff_bias_desc = dd.bias_desc = zero_md(); const bool is_fwd = one_of(prop_kind, forward_training, forward_inference); const bool with_bias = bias_desc && bias_desc->format_kind != format_kind::undef; const bool with_groups = weights_desc->ndims == src_desc->ndims + 1; bool runtime_dims_or_strides = memory_desc_wrapper(src_desc).has_runtime_dims_or_strides() || memory_desc_wrapper(weights_desc).has_runtime_dims_or_strides() || memory_desc_wrapper(dst_desc).has_runtime_dims_or_strides(); if (with_bias) runtime_dims_or_strides = runtime_dims_or_strides || memory_desc_wrapper(bias_desc).has_runtime_dims_or_strides(); VCHECK_DECONV_UNIMPL( !runtime_dims_or_strides, VERBOSE_RUNTIMEDIM_UNSUPPORTED); (prop_kind == backward_data ? dd.diff_src_desc : dd.src_desc) = *src_desc; (is_fwd ? dd.dst_desc : dd.diff_dst_desc) = *dst_desc; (prop_kind == backward_weights ? dd.diff_weights_desc : dd.weights_desc) = *weights_desc; if (with_bias) (prop_kind == backward_weights ? dd.diff_bias_desc : dd.bias_desc) = *bias_desc; int sp_dims = src_desc->ndims - 2; utils::array_copy(dd.strides, strides, sp_dims); utils::array_copy(dd.padding[0], padding_l, sp_dims); utils::array_copy(dd.padding[1], padding_r, sp_dims); if (dilates) utils::array_copy(dd.dilates, dilates, sp_dims); else utils::array_set(dd.dilates, 0, sp_dims); dd.accum_data_type = types::default_accum_data_type(src_desc->data_type, weights_desc->data_type, dst_desc->data_type, prop_kind); VCHECK_DECONV(dd.accum_data_type != data_type::undef, VERBOSE_INVALID_DATATYPE, "accumulation"); const dim_t g = with_groups ? weights_desc->dims[0] : 1; VCHECK_DECONV(src_desc->ndims == dst_desc->ndims, VERBOSE_INCONSISTENT_NDIMS_WITH_VALS, "src", "dst", src_desc->ndims, dst_desc->ndims); VCHECK_DECONV(utils::one_of(src_desc->ndims, 3, 4, 5), VERBOSE_BAD_NDIMS, "src", src_desc->ndims); VCHECK_DECONV(utils::one_of(weights_desc->ndims, src_desc->ndims, src_desc->ndims + 1), VERBOSE_INCONSISTENT_NDIMS_WITH_VALS, "src", "weights", weights_desc->ndims, src_desc->ndims); VCHECK_DECONV(IMPLICATION(with_bias, bias_desc->ndims == 1), VERBOSE_BAD_NDIMS, "bias", bias_desc->ndims); VCHECK_DECONV( IMPLICATION(with_bias, bias_desc->dims[0] == dst_desc->dims[1]), VERBOSE_INCONSISTENT_DIM, "bias", 0, "dst", 1); VCHECK_DECONV(src_desc->dims[0] == dst_desc->dims[0], VERBOSE_INCONSISTENT_DIM, "src", 0, "dst", 0); VCHECK_DECONV(src_desc->dims[1] == g * weights_desc->dims[with_groups + 1], VERBOSE_INCONSISTENT_DIM, "src", 1, "weights", with_groups + 1); VCHECK_DECONV(dst_desc->dims[1] == g * weights_desc->dims[with_groups + 0], VERBOSE_INCONSISTENT_DIM, "dst", 1, "weights", with_groups + 0); for (int i = 2; i < src_desc->ndims; ++i) { dim_t src = src_desc->dims[i]; dim_t ker = weights_desc->dims[with_groups + i]; dim_t dil = dd.dilates[i - 2]; dim_t pad_l = padding_l[i - 2]; dim_t pad_r = padding_r[i - 2]; dim_t str = strides[i - 2]; dim_t dst = dst_desc->dims[i]; dim_t ker_range = 1 + (ker - 1) * (dil + 1); VCHECK_DECONV(str > 0, VERBOSE_BAD_DIM, "strides", i - 2); VCHECK_DECONV(dil >= 0, "%s: dilation (%d) must be non-negative", VERBOSE_INCONSISTENT_PRB, static_cast(dil)); VCHECK_DECONV(pad_l >= 0, "%s: left padding value (%d) must be non-negative", VERBOSE_INCONSISTENT_PRB, static_cast(pad_l)); VCHECK_DECONV(pad_r + str > 0, "%s: right padding (%d) and stride (%d) must sum up to a " "positive value", VERBOSE_INCONSISTENT_PRB, static_cast(pad_r), static_cast(str)); VCHECK_DECONV((dst - ker_range + pad_l + pad_r) / str + 1 == src, "%s: mismatch between actual and computed src dims, src (%d) " "!= (dst(%d) - ker(%d) + pad_l(%d) + pad_r(%d))/ str(%d) + 1", VERBOSE_INCONSISTENT_PRB, static_cast(src), static_cast(dst), static_cast(ker_range), static_cast(pad_l), static_cast(pad_r), static_cast(str)); } *deconv_desc = dd; return success; } status_t deconv_attr_check(const deconvolution_desc_t &desc, const engine_t *engine, const primitive_attr_t *attr) { using smask_t = primitive_attr_t::skip_mask_t; if (attr == nullptr) return status::success; if (attr->has_default_values()) return status::success; // Check attributes if (utils::one_of(desc.prop_kind, prop_kind::forward_inference, prop_kind::forward_training)) { const data_type_t src_dt = desc.src_desc.data_type; const data_type_t dst_dt = desc.dst_desc.data_type; auto fwd_attr_mask = smask_t::post_ops | smask_t::sum_dt | smask_t::fpmath_mode; bool is_int8 = utils::one_of(src_dt, data_type::s8, data_type::u8); if (engine->kind() == engine_kind::gpu) is_int8 = is_int8 || utils::one_of(dst_dt, data_type::s8, data_type::u8, data_type::s32); if (is_int8) fwd_attr_mask |= smask_t::scales | smask_t::zero_points; VCHECK_DECONV_UNIMPL(attr->has_default_values(fwd_attr_mask, dst_dt), VERBOSE_UNSUPPORTED_ATTR); // Check scales if (!attr->scales_.has_default_values()) { const auto &sc = attr->scales_; const bool with_groups = desc.src_desc.ndims != desc.weights_desc.ndims; VCHECK_DECONV_UNIMPL( IMPLICATION(!sc.has_default_values(DNNL_ARG_SRC), sc.get_mask(DNNL_ARG_SRC) == 0), VERBOSE_UNSUPPORTED_SCALES_CFG); VCHECK_DECONV_UNIMPL( IMPLICATION(!sc.has_default_values(DNNL_ARG_WEIGHTS), utils::one_of(sc.get_mask(DNNL_ARG_WEIGHTS), 0, with_groups ? 3 : 1)), VERBOSE_UNSUPPORTED_SCALES_CFG); VCHECK_DECONV_UNIMPL( IMPLICATION(!sc.has_default_values(DNNL_ARG_DST), sc.get_mask(DNNL_ARG_DST) == 0), VERBOSE_UNSUPPORTED_SCALES_CFG); } // Check zero points if (!attr->zero_points_.has_default_values()) { const auto &zp = attr->zero_points_; VCHECK_DECONV_UNIMPL( IMPLICATION(!zp.has_default_values(DNNL_ARG_SRC), utils::one_of( zp.get_mask(DNNL_ARG_SRC), 0, 1 << 1)), VERBOSE_UNSUPPORTED_ZP_CFG); VCHECK_DECONV_UNIMPL(zp.has_default_values(DNNL_ARG_WEIGHTS), VERBOSE_UNSUPPORTED_ZP_CFG); VCHECK_DECONV_UNIMPL( IMPLICATION(!zp.has_default_values(DNNL_ARG_DST), utils::one_of( zp.get_mask(DNNL_ARG_DST), 0, 1 << 1)), VERBOSE_UNSUPPORTED_ZP_CFG); } // Check post-ops if (!attr->post_ops_.has_default_values()) { const auto &po = attr->post_ops_; using namespace primitive_kind; VCHECK_DECONV_UNIMPL( po.has_default_values({binary, eltwise, prelu, sum}), VERBOSE_UNSUPPORTED_POSTOP); // Check sum VCHECK_DECONV_UNIMPL( po.check_sum_consistency(dst_dt, is_int8, true), VERBOSE_UNSUPPORTED_POSTOP); // Note: verbose support is inside the call. CHECK(po.validate_binary(engine->kind(), &desc.dst_desc)); } } else { auto bwd_attr_mask = smask_t::fpmath_mode; VCHECK_DECONV_UNIMPL(attr->has_default_values(bwd_attr_mask), VERBOSE_UNSUPPORTED_ATTR); } return status::success; } } // namespace status_t dnnl_deconvolution_forward_primitive_desc_create( primitive_desc_iface_t **primitive_desc_iface, engine_t *engine, prop_kind_t prop_kind, alg_kind_t alg_kind, const memory_desc_t *src_desc, const memory_desc_t *weights_desc, const memory_desc_t *bias_desc, const memory_desc_t *dst_desc, const dims_t strides, const dims_t dilates, const dims_t padding_l, const dims_t padding_r, const primitive_attr_t *attr) { if (!one_of(prop_kind, forward_training, forward_inference)) return invalid_arguments; auto deconv_desc = deconvolution_desc_t(); CHECK(deconv_desc_init(&deconv_desc, prop_kind, alg_kind, src_desc, weights_desc, bias_desc, dst_desc, strides, dilates, padding_l, padding_r)); CHECK(deconv_attr_check(deconv_desc, engine, attr)); return primitive_desc_create(primitive_desc_iface, engine, (const op_desc_t *)&deconv_desc, nullptr, attr); } status_t dnnl_deconvolution_backward_data_primitive_desc_create( primitive_desc_iface_t **primitive_desc_iface, engine_t *engine, alg_kind_t alg_kind, const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc, const memory_desc_t *diff_dst_desc, const dims_t strides, const dims_t dilates, const dims_t padding_l, const dims_t padding_r, const primitive_desc_iface_t *hint_fwd_pd, const primitive_attr_t *attr) { auto deconv_desc = deconvolution_desc_t(); CHECK(deconv_desc_init(&deconv_desc, backward_data, alg_kind, diff_src_desc, weights_desc, nullptr, diff_dst_desc, strides, dilates, padding_l, padding_r)); CHECK(deconv_attr_check(deconv_desc, engine, attr)); return primitive_desc_create(primitive_desc_iface, engine, (const op_desc_t *)&deconv_desc, hint_fwd_pd, attr); } status_t dnnl_deconvolution_backward_weights_primitive_desc_create( primitive_desc_iface_t **primitive_desc_iface, engine_t *engine, alg_kind_t alg_kind, const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc, const memory_desc_t *diff_bias_desc, const memory_desc_t *diff_dst_desc, const dims_t strides, const dims_t dilates, const dims_t padding_l, const dims_t padding_r, const primitive_desc_iface_t *hint_fwd_pd, const primitive_attr_t *attr) { auto deconv_desc = deconvolution_desc_t(); CHECK(deconv_desc_init(&deconv_desc, backward_weights, alg_kind, src_desc, diff_weights_desc, diff_bias_desc, diff_dst_desc, strides, dilates, padding_l, padding_r)); CHECK(deconv_attr_check(deconv_desc, engine, attr)); return primitive_desc_create(primitive_desc_iface, engine, (const op_desc_t *)&deconv_desc, hint_fwd_pd, attr); } // vim: et ts=4 sw=4 cindent cino+=l0,\:4,N-s