1/*******************************************************************************
2* Copyright 2020-2022 Intel Corporation
3*
4* Licensed under the Apache License, Version 2.0 (the "License");
5* you may not use this file except in compliance with the License.
6* You may obtain a copy of the License at
7*
8* http://www.apache.org/licenses/LICENSE-2.0
9*
10* Unless required by applicable law or agreed to in writing, software
11* distributed under the License is distributed on an "AS IS" BASIS,
12* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13* See the License for the specific language governing permissions and
14* limitations under the License.
15*******************************************************************************/
16
17#ifndef CPU_X64_JIT_AVX512_CORE_AMX_CONVOLUTION_HPP
18#define CPU_X64_JIT_AVX512_CORE_AMX_CONVOLUTION_HPP
19
20#include "common/c_types_map.hpp"
21#include "common/dnnl_thread.hpp"
22#include "common/memory_tracking.hpp"
23#include "common/primitive.hpp"
24#include "common/utils.hpp"
25
26#include "cpu/cpu_convolution_pd.hpp"
27
28#include "cpu/x64/amx_tile_configure.hpp"
29#include "cpu/x64/cpu_barrier.hpp"
30#include "cpu/x64/cpu_reducer.hpp"
31#include "cpu/x64/jit_avx512_core_amx_conv_kernel.hpp"
32#include "cpu/x64/jit_transpose_utils.hpp"
33
34namespace dnnl {
35namespace impl {
36namespace cpu {
37namespace x64 {
38
39struct jit_avx512_core_amx_convolution_fwd_t : public primitive_t {
40 struct pd_t : public cpu_convolution_fwd_pd_t {
41 pd_t(const convolution_desc_t *adesc, const primitive_attr_t *attr,
42 const typename pd_t::base_class *hint_fwd_pd)
43 : cpu_convolution_fwd_pd_t(adesc, attr, hint_fwd_pd), jcp_() {}
44
45 DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit:", jcp_.isa, ""),
46 jit_avx512_core_amx_convolution_fwd_t);
47
48 status_t init(engine_t *engine) {
49 using namespace data_type;
50 using smask_t = primitive_attr_t::skip_mask_t;
51 bool is_bf16_convolution
52 = (src_md(0)->data_type == bf16
53 && weights_md(0)->data_type == bf16
54 && utils::one_of(dst_md(0)->data_type, f32, bf16))
55 && IMPLICATION(with_bias(),
56 utils::one_of(weights_md(1)->data_type, f32, bf16))
57 && attr()->has_default_values(smask_t::post_ops);
58 bool is_int8_convolution
59 = utils::one_of(src_md(0)->data_type, s8, u8)
60 && weights_md(0)->data_type == s8
61 && utils::one_of(
62 dst_md(0)->data_type, s8, u8, s32, f32, bf16)
63 && IMPLICATION(with_bias(),
64 utils::one_of(
65 weights_md(1)->data_type, f32, s32, s8, u8))
66 && attr()->has_default_values(smask_t::scales_runtime
67 | smask_t::post_ops
68 | smask_t::zero_points_runtime
69 | smask_t::sum_dt,
70 dst_md(0)->data_type)
71 && attr()->post_ops_.check_sum_consistent_dt(
72 dst_md(0)->data_type);
73
74 bool ok = is_fwd()
75 && set_default_alg_kind(alg_kind::convolution_direct)
76 && (is_bf16_convolution || is_int8_convolution)
77 && !has_zero_dim_memory() && zero_points_ok();
78 if (!ok) return status::unimplemented;
79
80 CHECK(jit_avx512_core_amx_fwd_kernel_t::init_conf(jcp_, *desc(),
81 src_md_, weights_md_, dst_md_, bias_md_, attr_,
82 dnnl_get_max_threads()));
83
84 auto scratchpad = scratchpad_registry().registrar();
85 CHECK(jit_avx512_core_amx_fwd_kernel_t::init_scratchpad(
86 scratchpad, jcp_, *attr()));
87
88 return status::success;
89 }
90
91 jit_conv_conf_t jcp_;
92
93 protected:
94 bool zero_points_ok() const {
95 // Only common zero points are supported -> mask should only be 0
96 int mask_src = 0, mask_dst = 0;
97 attr()->zero_points_.get(DNNL_ARG_SRC, &mask_src);
98 attr()->zero_points_.get(DNNL_ARG_DST, &mask_dst);
99 return attr()->zero_points_.has_default_values(DNNL_ARG_WEIGHTS)
100 && mask_src == 0 && mask_dst == 0;
101 }
102 };
103
104 jit_avx512_core_amx_convolution_fwd_t(const pd_t *apd) : primitive_t(apd) {}
105
106 status_t init(engine_t *engine) override {
107 CHECK(safe_ptr_assign(kernel_,
108 new jit_avx512_core_amx_fwd_kernel_t(
109 pd()->jcp_, *pd()->attr(), *pd()->dst_md(0))));
110 return kernel_->create_kernel();
111 }
112
113 status_t execute(const exec_ctx_t &ctx) const override {
114 const auto &_pd = pd();
115 if (_pd->jcp_.is_depthwise)
116 return status::unimplemented;
117 else if (_pd->jcp_.is_relo)
118 return execute_forward_reduced_lowering(ctx);
119 return execute_forward(ctx);
120 }
121
122private:
123 status_t execute_forward_reduced_lowering(const exec_ctx_t &ctx) const;
124 status_t execute_forward(const exec_ctx_t &ctx) const;
125 const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
126 void prepare_padded_bias(const char *&bias,
127 const memory_tracking::grantor_t &scratchpad) const;
128
129 std::unique_ptr<jit_avx512_core_amx_fwd_kernel_t> kernel_;
130};
131
132template <impl::data_type_t diff_src_type, impl::data_type_t wei_type,
133 impl::data_type_t diff_dst_type>
134struct jit_avx512_core_amx_convolution_bwd_data_t : public primitive_t {
135 struct pd_t : public cpu_convolution_bwd_data_pd_t {
136 pd_t(const convolution_desc_t *adesc, const primitive_attr_t *attr,
137 const convolution_fwd_pd_t *hint_fwd_pd)
138 : cpu_convolution_bwd_data_pd_t(adesc, attr, hint_fwd_pd), jcp_() {}
139
140 DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit:", jcp_.isa, ""),
141 jit_avx512_core_amx_convolution_bwd_data_t);
142
143 status_t init(engine_t *engine) {
144 bool is_bf16_convolution = true
145 && (diff_dst_md_.data_type == data_type::bf16
146 && weights_md_.data_type == data_type::bf16
147 && utils::one_of(diff_src_md_.data_type,
148 data_type::f32, data_type::bf16))
149 && attr()->has_default_values();
150
151 bool ok = true && desc()->prop_kind == prop_kind::backward_data
152 && set_default_alg_kind(alg_kind::convolution_direct)
153 && is_bf16_convolution && !has_zero_dim_memory();
154 if (!ok) return status::unimplemented;
155
156 status_t status = jit_avx512_core_amx_bwd_data_kernel_t::init_conf(
157 jcp_, *desc(), diff_src_md_, weights_md_, diff_dst_md_,
158 nullptr /* no bias */, attr_, dnnl_get_max_threads());
159 if (status != status::success) return status;
160
161 auto scratchpad = scratchpad_registry().registrar();
162 jit_avx512_core_amx_bwd_data_kernel_t::init_scratchpad(
163 scratchpad, jcp_, *attr());
164
165 return status;
166 }
167
168 jit_conv_conf_t jcp_;
169 };
170
171 jit_avx512_core_amx_convolution_bwd_data_t(const pd_t *apd)
172 : primitive_t(apd) {}
173
174 typedef typename prec_traits<diff_src_type>::type diff_src_data_t;
175 typedef typename prec_traits<wei_type>::type wei_data_t;
176 typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
177
178 status_t init(engine_t *engine) override {
179 CHECK(safe_ptr_assign(kernel_,
180 new jit_avx512_core_amx_bwd_data_kernel_t(
181 pd()->jcp_, *pd()->attr())));
182 return kernel_->create_kernel();
183 }
184
185 status_t execute(const exec_ctx_t &ctx) const override {
186 const auto &_pd = pd();
187 if (_pd->jcp_.is_depthwise) {
188 assert(!"_pd->jcp_.is_depthwise not implemented");
189 return status::unimplemented;
190 } else
191 return execute_backward(ctx);
192 }
193
194private:
195 status_t execute_backward(const exec_ctx_t &ctx) const;
196 const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
197
198 std::unique_ptr<jit_avx512_core_amx_bwd_data_kernel_t> kernel_;
199};
200
201struct jit_avx512_core_amx_convolution_bwd_weights_t : public primitive_t {
202 struct pd_t : public cpu_convolution_bwd_weights_pd_t {
203 pd_t(const convolution_desc_t *adesc, const primitive_attr_t *attr,
204 const convolution_fwd_pd_t *hint_fwd_pd)
205 : cpu_convolution_bwd_weights_pd_t(adesc, attr, hint_fwd_pd)
206 , jcp_() {}
207
208 DECLARE_COMMON_PD_T(JIT_IMPL_NAME_HELPER("jit:", jcp_.isa, ""),
209 jit_avx512_core_amx_convolution_bwd_weights_t);
210
211 status_t init(engine_t *engine) {
212 bool ok = true && is_bwd_w()
213 && set_default_alg_kind(alg_kind::convolution_direct)
214 && (expect_data_types(data_type::bf16, data_type::bf16,
215 data_type::undef, data_type::bf16,
216 data_type::undef)
217 || expect_data_types(data_type::bf16,
218 data_type::f32, data_type::undef,
219 data_type::bf16, data_type::undef))
220 && IMPLICATION(with_bias(),
221 utils::one_of(diff_bias_md_.data_type,
222 data_type::f32, data_type::bf16))
223 && attr()->has_default_values() && !has_zero_dim_memory();
224 if (!ok) return status::unimplemented;
225
226 status_t status
227 = jit_avx512_core_amx_bwd_weights_kernel_t::init_conf(jcp_,
228 *desc(), src_md_, diff_weights_md_, diff_bias_md_,
229 diff_dst_md_, dnnl_get_max_threads());
230 if (status != status::success) return status;
231
232 auto scratchpad = scratchpad_registry().registrar();
233 status = jit_avx512_core_amx_bwd_weights_kernel_t::init_scratchpad(
234 scratchpad, jcp_, src_md_, diff_weights_md_, diff_dst_md_);
235 if (status != status::success) return status;
236
237 return status;
238 }
239
240 jit_conv_conf_t jcp_;
241 };
242
243 jit_avx512_core_amx_convolution_bwd_weights_t(const pd_t *apd)
244 : primitive_t(apd) {}
245
246 typedef typename prec_traits<data_type::bf16>::type src_data_t;
247 typedef typename prec_traits<data_type::bf16>::type diff_dst_data_t;
248
249 status_t init(engine_t *engine) override;
250
251 status_t execute(const exec_ctx_t &ctx) const override {
252 execute_backward_weights(ctx);
253 return status::success;
254 }
255
256private:
257 struct thread_info_t;
258
259 void execute_backward_weights(const exec_ctx_t &ctx) const;
260 void prepare_scratchpad_data(const exec_ctx_t &ctx) const;
261 void compute_diff_weights_2d(const thread_info_t *) const;
262 void compute_diff_weights_3d(const thread_info_t *) const;
263 void compute_diff_weights(const thread_info_t *) const;
264 void reduce_and_convert_diff_weights_and_bias(const thread_info_t *) const;
265 void store_in_vnni_format(const thread_info_t *) const;
266
267 const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
268
269 size_t tr_src_buf_number(const thread_info_t *ti, int g, int ic) const;
270 size_t tr_diff_dst_buf_number(const thread_info_t *ti, int g, int oc) const;
271 void trans_src_nxc(src_data_t *tr_src, const src_data_t *src_base,
272 int spatial_start, dim_t spatial_start_offset, int icb_start,
273 dim_t chb_stride, int my_work) const;
274 void trans_dst_nxc(diff_dst_data_t *tr_diff_dst,
275 const diff_dst_data_t *diff_dst_base, int spatial_start,
276 dim_t spatial_start_offset, int ocb_start, dim_t chb_stride,
277 int my_work) const;
278
279 int nthr_ = 0, nthr_mb_ = 0, nthr_g_ = 0, nthr_oc_b_ = 0, nthr_ic_b_ = 0;
280
281 std::unique_ptr<jit_avx512_core_amx_bwd_weights_kernel_t> kernel_;
282
283 std::unique_ptr<cpu_accumulator_1d_t<data_type::f32>> acc_ker_;
284
285 std::unique_ptr<jit_diff_wei_trans_to_vnni_t> diff_wei_trans_kernel_;
286 std::unique_ptr<jit_trans_src_t> trans_kernel_;
287 std::unique_ptr<jit_trans_dst_t> trans_dst_kernel_;
288
289 inline dim_t wei_offset_int(int g, int oc_b, int ic_b, int kX) const {
290 const auto &jcp = kernel_->jcp;
291 const dim_t const_extra_offset = jcp.kw * jcp.ic_block * jcp.oc_block;
292 dim_t extra_offset = (jcp.ndims == 5) ? kX * jcp.kh * const_extra_offset
293 : kX * const_extra_offset;
294 return (dim_t)((g * jcp.nb_oc + oc_b) * jcp.nb_ic + ic_b) * jcp.kd
295 * jcp.kh * jcp.kw * jcp.ic_block * jcp.oc_block
296 + extra_offset;
297 }
298 inline dim_t wei_offset_ext(int g, int oc_b, int ic_b, int kX) const {
299 const auto &jcp = kernel_->jcp;
300 const int nb_ic = utils::div_up(jcp.ic, 2 * jcp.ic_block);
301 const dim_t const_extra_offset
302 = jcp.kw * jcp.ic_block * jcp.oc_block * 2;
303 dim_t extra_offset = (jcp.ndims == 5) ? kX * jcp.kh * const_extra_offset
304 : kX * const_extra_offset;
305 return (dim_t)((g * jcp.nb_oc + oc_b) * nb_ic + ic_b) * jcp.kd * jcp.kh
306 * jcp.kw * jcp.ic_block * jcp.oc_block * 2
307 + extra_offset;
308 }
309};
310
311} // namespace x64
312} // namespace cpu
313} // namespace impl
314} // namespace dnnl
315
316#endif
317
318// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
319