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This commit is contained in:
Meng, Hengyu 2024-08-20 08:23:41 +00:00
parent a07c32ea54
commit b180cb352b
7 changed files with 208 additions and 4 deletions
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11
ggml/src/ggml-sycl.cpp
View File

@ -38,7 +38,9 @@
#include "ggml-sycl/backend.hpp"
#include "ggml-sycl/presets.hpp"
#include "ggml-sycl/gemm.hpp"
#if GGML_SYCL_DNNL
#include "ggml-sycl/onednn/gemm.hpp"
#endif
bool ggml_sycl_loaded(void);
void ggml_sycl_free_data(struct ggml_tensor * tensor);
@ -3892,6 +3894,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens
ggml_sycl_func_t func;
switch (tensor->op) {
case GGML_OP_CONV_TRANSPOSE_2D:
func = ggml_sycl_op_conv_2d;
break;
case GGML_OP_CONV_TRANSPOSE_1D:
func = ggml_sycl_op_conv_transpose_1d;
break;
@ -5007,6 +5012,10 @@ GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t back
GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
switch (op->op) {
case GGML_OP_CONV_TRANSPOSE_2D:
{
return true;
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
ggml_type src0_type = op->src[0]->type;

28
ggml/src/ggml-sycl/conv.cpp
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@ -11,6 +11,7 @@
//
#include "conv.hpp"
#include "onednn/convolution.hpp"
static void conv_transpose_1d_kernel(
const int s0, const int output_size,
@ -97,3 +98,30 @@ void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, const ggml_
src0_d, src1_d, dst_d, stream);
}
void ggml_sycl_op_conv_2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst) {
const void * src0_d = (const void *)src0->data;
const void * src1_d = (const void *)src1->data;
void * dst_d = (void *)dst->data;
auto dnnl_stream = ctx.stream_dnnl(ctx.stream());
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
const int32_t * opts = (const int32_t *)dst->op_params;
DnnlConvWrapper::conv_params params = {
opts[0], opts[1], opts[2], opts[3], opts[4], opts[5], true
};
DnnlConvWrapper::forward(dnnl_stream,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
src1->ne[1], src1->ne[2], src1->ne[3],
params,
src0_d, DnnlConvWrapper::to_dt<float>(),
src1_d, DnnlConvWrapper::to_dt<float>(),
dst_d, DnnlConvWrapper::to_dt<float>());
}

3
ggml/src/ggml-sycl/conv.hpp
View File

@ -18,4 +18,7 @@
void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst);
void ggml_sycl_op_conv_2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst);
#endif // GGML_SYCL_CONV_HPP

104
ggml/src/ggml-sycl/onednn/convolution.hpp Normal file
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@ -0,0 +1,104 @@
//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
#ifndef GGML_SYCL_ONEDNN_CONV_HPP
#define GGML_SYCL_ONEDNN_CONV_HPP
#include <fstream>
#include <iostream>
#include "ggml-sycl.h"
#if GGML_SYCL_DNNL
#include "dnnl.hpp"
#include "dnnl_sycl.hpp"
class DnnlConvWrapper {
public:
using dt = dnnl::memory::data_type;
using tag = dnnl::memory::format_tag;
struct conv_params {
int s0;
int s1;
int p0;
int p1;
int d0;
int d1;
bool is_2d;
};
template<typename T>
static constexpr dt to_dt() {
if constexpr (std::is_same_v<T, float>) return dt::f32;
else if constexpr (std::is_same_v<T, sycl::half>) return dt::f16;
else static_assert(0);
}
static inline void forward(const dnnl::stream& stream,
int n, int h, int w, int ic, int oc, int kh, int kw,
conv_params& params,
const void* a, dt at, const void* b, dt bt, void* c, dt ct)
{
auto const eng = stream.get_engine();
dnnl::memory::dims a_dims, b_dims, c_dims;
dnnl::memory::desc a_md, b_md, c_md, bias_md;
dnnl::primitive_attr pattr;
if(params.is_2d) {
a_dims = { n, ic, h, w };
b_dims = { oc, ic, kh, kw };
c_dims = { n, oc, h, w };
a_md = dnnl::memory::desc(a_dims, at, tag::nchw);
b_md = dnnl::memory::desc(b_dims, bt, tag::oihw);
c_md = dnnl::memory::desc(c_dims, ct, tag::nchw);
} else {
a_dims = { n, ic, h };
b_dims = { oc, ic, kh };
c_dims = { n, oc, h };
a_md = dnnl::memory::desc(a_dims, at, tag::ncw);
b_md = dnnl::memory::desc(b_dims, bt, tag::oiw);
c_md = dnnl::memory::desc(c_dims, ct, tag::ncw);
}
auto a_mem = dnnl::memory(a_md, eng, (void*)a);
auto b_mem = dnnl::memory(b_md, eng, (void*)b);
// Create the primitive.
auto conv_fwd_pd = dnnl::convolution_forward::primitive_desc(
eng,
dnnl::prop_kind::forward,
dnnl::algorithm::convolution_direct,
a_md,
b_md,
bias_md,
c_md,
{params.s0, params.s1},
{params.d0, params.d1},
{params.p0, params.p1},
{params.p0, params.p1},
pattr);
auto conv_fwd = dnnl::convolution_forward(conv_fwd_pd);
auto c_mem = dnnl::memory(conv_fwd_pd.dst_desc(), eng, c);
// Primitive arguments.
std::unordered_map<int, dnnl::memory> conv_args;
conv_args.insert({ DNNL_ARG_SRC, a_mem });
conv_args.insert({ DNNL_ARG_WEIGHTS, b_mem });
conv_args.insert({ DNNL_ARG_DST, c_mem });
conv_fwd.execute(stream, conv_args);
}
};
#endif
#endif // GGML_SYCL_ONEDNN_CONV_HPP

6
ggml/src/ggml-sycl/gemm.hpp → ggml/src/ggml-sycl/onednn/gemm.hpp
View File

@ -10,8 +10,8 @@
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
#ifndef GGML_SYCL_GEMM_HPP
#define GGML_SYCL_GEMM_HPP
#ifndef GGML_SYCL_ONEDNN_GEMM_HPP
#define GGML_SYCL_ONEDNN_GEMM_HPP
#include <fstream>
#include <iostream>
@ -98,4 +98,4 @@ public:
#endif
#endif // GGML_SYCL_GEMM_HPP
#endif // GGML_SYCL_ONEDNN_GEMM_HPP

30
ggml/src/ggml.c
View File

@ -6770,6 +6770,35 @@ struct ggml_tensor * ggml_conv_2d(
int p1,
int d0,
int d1) {
#ifdef GGML_SYCL_DNNL
bool is_node = false;
if (a->grad || b->grad) {
GGML_ABORT("fatal error"); // TODO: implement backward
is_node = true;
}
const int64_t OH = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1);
const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
const int64_t ne[4] = {
OW,
OH,
a->ne[3], // OC
b->ne[3], // N
};
struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne);
int32_t params[] = { s0, s1, p0, p1, d0, d1};
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_CONV_TRANSPOSE_2D;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = b;
return result;
#else
struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW]
struct ggml_tensor * result =
@ -6782,6 +6811,7 @@ struct ggml_tensor * ggml_conv_2d(
return result;
#endif
}
// ggml_conv_2d_sk_p0

30
tests/test-backend-ops.cpp
View File

@ -1308,6 +1308,35 @@ struct test_conv_transpose_1d : public test_case {
}
};
struct test_conv_2d : public test_case {
const std::array<int64_t, 4> ne_input;
const std::array<int64_t, 4> ne_kernel;
const int s0; // stride
const int p0; // padding
const int d0; // dilation
const int s1; // stride
const int p1; // padding
const int d1; // dilation
std::string vars() override {
return VARS_TO_STR5(ne_input, ne_kernel, s0, p0, d0);
}
test_conv_2d(std::array<int64_t, 4> ne_input = {197, 32, 1, 1}, // [input_width, input_height, input_channels, 1]
std::array<int64_t, 4> ne_kernel = {16, 32, 1, 32}, // [kernel_width, kernel_height, input_channels, 1]
int s0 = 1, int p0 = 0, int d0 = 1,
int s1 = 1, int p1 = 0, int d1 = 1)
: ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), p0(p0), d0(d0), s1(s1), p1(p1), d1(d1){}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data());
ggml_tensor * out = ggml_conv_2d(ctx, kernel, input, s0, s1, p0, p1, d0, d1);
return out;
}
};
// GGML_OP_IM2COL
struct test_im2col : public test_case {
const ggml_type type_input;
@ -2160,6 +2189,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,2,2,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_2d());
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));