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[SYCL] Support gpt-oss by OPs add-id, mul_mat for mxfp4, swiglu_oai (#17826) 

* support gpt-oss GPU by OP add-id, mul_mat for mxfp4, swiglu_oai, fix warning

* fix fault ut case, update ops.md

* rebase, fix format issue
This commit is contained in:
Neo Zhang Jianyu 2025-12-15 10:35:15 +08:00 committed by GitHub
parent 745fa0e78b
commit 4aced7a631
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15 changed files with 1201 additions and 385 deletions
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18
docs/ops.md
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@ -18,12 +18,12 @@ Legend:
| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
| CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
@ -31,7 +31,7 @@ Legend:
| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
@ -64,7 +64,7 @@ Legend:
| IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
@ -98,14 +98,14 @@ Legend:
| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
| SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@ -113,7 +113,7 @@ Legend:
| SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ |
| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |

1158
docs/ops/SYCL.csv
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77
ggml/src/ggml-sycl/add-id.cpp Normal file
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@ -0,0 +1,77 @@
#include <sycl/sycl.hpp>
#include "common.hpp"
#include "add-id.hpp"
static void add_id_kernel(
const float* src0,
const float* src1,
const int32_t* src2,
float* dst,
int64_t ne0,
int64_t ne1,
size_t nb01,
size_t nb02,
size_t nb11,
size_t nb21,
sycl::nd_item<3> item_ct1) {
const int64_t i1 = item_ct1.get_group(2);
const int64_t i2 = item_ct1.get_group(1);
const int i11 =
*(const int32_t*)((const char*)src2 + i1 * sizeof(int32_t) + i2 * nb21);
const size_t nb1 = ne0 * sizeof(float);
const size_t nb2 = ne1 * nb1;
float* dst_row = (float*)((char*)dst + i1 * nb1 + i2 * nb2);
const float* src0_row =
(const float*)((const char*)src0 + i1 * nb01 + i2 * nb02);
const float* src1_row = (const float*)((const char*)src1 + i11 * nb11);
for (int64_t i0 = item_ct1.get_local_id(2); i0 < ne0;
i0 += item_ct1.get_local_range(2)) {
dst_row[i0] = src0_row[i0] + src1_row[i0];
}
}
void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
const ggml_tensor* src0 = dst->src[0];
const ggml_tensor* src1 = dst->src[1];
const ggml_tensor* src2 = dst->src[2];
GGML_TENSOR_TERNARY_OP_LOCALS
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(src2->type == GGML_TYPE_I32);
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(nb10 == sizeof(float));
GGML_ASSERT(nb20 == sizeof(int32_t));
const float* src0_d = (const float*)src0->data;
const float* src1_d = (const float*)src1->data;
const int32_t* src2_d = (const int32_t*)src2->data;
float* dst_d = (float*)dst->data;
int threads = std::min((int)ne00, 768); // cols
ctx.stream()->parallel_for(
sycl::nd_range<3>(
sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads),
sycl::range<3>(1, 1, threads)),
[=](sycl::nd_item<3> item_ct1) {
add_id_kernel(
src0_d,
src1_d,
src2_d,
dst_d,
ne0,
ne1,
nb01,
nb02,
nb11,
nb21,
item_ct1);
});
}

8
ggml/src/ggml-sycl/add-id.hpp Normal file
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@ -0,0 +1,8 @@
#ifndef GGML_SYCL_ADD_ID_HPP
#define GGML_SYCL_ADD_ID_HPP
#include "common.hpp"
void ggml_sycl_add_id(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
#endif // GGML_SYCL_ADD_ID_HPP

17
ggml/src/ggml-sycl/common.hpp
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@ -642,5 +642,22 @@ static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3
return sycl::uint2(div_val, mod_val);
}
static __dpct_inline__ int ggml_sycl_dp4a(const int a, const int b, int c) {
return dpct::dp4a(a, b, c);
}
static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) {
uint32_t bits;
if (x == 0) {
bits = 0x00400000;
} else {
bits = (uint32_t) x << 23;
}
float result;
memcpy(&result, &bits, sizeof(float));
return result;
}
#endif // GGML_SYCL_COMMON_HPP

15
ggml/src/ggml-sycl/convert.cpp
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@ -472,6 +472,16 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k
}
}
template <typename dst_t>
static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) {
const int nb = (k + QK_K - 1) / QK_K;
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_mxfp4(vx, y, item_ct1);
});
}
template <typename src_t, typename dst_t>
static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03,
@ -518,6 +528,7 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct
convert_unary_nc_sycl<src_t>(vx, y, k, 1, 1, 1, k, k, k, queue);
}
to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
switch (type) {
case GGML_TYPE_Q4_0:
@ -571,6 +582,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
return dequantize_row_iq4_xs_sycl;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_sycl;
case GGML_TYPE_MXFP4:
return dequantize_row_mxfp4_sycl;
case GGML_TYPE_F32:
return convert_unary_sycl<float>;
#ifdef GGML_SYCL_HAS_BF16
@ -636,6 +649,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
return dequantize_row_iq4_xs_sycl;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_sycl;
case GGML_TYPE_MXFP4:
return dequantize_row_mxfp4_sycl;
case GGML_TYPE_F16:
return convert_unary_sycl<sycl::half>;
#ifdef GGML_SYCL_HAS_BF16

18
ggml/src/ggml-sycl/dequantize.hpp
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@ -819,5 +819,23 @@ dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy,
}
}
template<typename dst_t>
static void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy,
const sycl::nd_item<3> &item_ct1) {
// auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
const int64_t i = item_ct1.get_group(2);
const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
const int64_t tid = item_ct1.get_local_id(2);
const int64_t il = tid/8; // 0...3
const int64_t ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 4*il;
const uint8_t * q4 = x[ib].qs + 4*il;
const float d = ggml_sycl_e8m0_to_fp32(x[ib].e);
for (int j = 0; j < 4; ++j) {
y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
y[j+16] = d * kvalues_mxfp4[q4[j] >> 4]*0.5f;
}
}
#endif // GGML_SYCL_DEQUANTIZE_HPP

59
ggml/src/ggml-sycl/dpct/helper.hpp
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@ -1860,10 +1860,31 @@ namespace dpct
: id);
}
template <typename T1, typename T2>
using dot_product_acc_t = std::conditional_t<
std::is_unsigned_v<T1> && std::is_unsigned_v<T2>,
uint32_t,
int32_t>;
template <typename T>
sycl::vec<T, 4> extract_and_sign_or_zero_extend4(T val) {
return sycl::vec<T, 1>(val)
.template as<sycl::vec<
std::conditional_t<std::is_signed_v<T>, int8_t, uint8_t>,
4>>()
.template convert<T>();
}
template <typename T1, typename T2, typename T3>
inline auto dp4a(T1 a, T2 b, T3 c)
{
return syclcompat::dp4a(a, b, c);
inline auto dp4a(T1 a, T2 b, T3 c) {
dot_product_acc_t<T1, T2> res = c;
auto va = extract_and_sign_or_zero_extend4(a);
auto vb = extract_and_sign_or_zero_extend4(b);
res += va[0] * vb[0];
res += va[1] * vb[1];
res += va[2] * vb[2];
res += va[3] * vb[3];
return res;
}
struct sub_sat
@ -2972,6 +2993,38 @@ namespace dpct
atomic_fetch_add<T1, addressSpace>(addr, operand, memoryOrder);
}
inline unsigned int byte_level_permute(
unsigned int a, unsigned int b, unsigned int s) {
unsigned int ret;
ret = ((((std::uint64_t)b << 32 | a) >> (s & 0x7) * 8) & 0xff) |
(((((std::uint64_t)b << 32 | a) >> ((s >> 4) & 0x7) * 8) & 0xff)
<< 8) |
(((((std::uint64_t)b << 32 | a) >> ((s >> 8) & 0x7) * 8) & 0xff)
<< 16) |
(((((std::uint64_t)b << 32 | a) >> ((s >> 12) & 0x7) * 8) & 0xff)
<< 24);
return ret;
}
inline uint32_t byte_level_permute_custom(
uint32_t low32, uint32_t high32, uint32_t sel, int mode = 0) {
constexpr uint16_t lookup[6][4] = {
{0x3210, 0x4321, 0x5432, 0x6543}, // Forward 4-byte extract
{0x5670, 0x6701, 0x7012, 0x0123}, // Backward 4-byte extract
{0x0000, 0x1111, 0x2222, 0x3333}, // Replicate 8-bit values
{0x3210, 0x3211, 0x3222, 0x3333}, // Edge clamp left
{0x0000, 0x1110, 0x2210, 0x3210}, // Edge clamp right
{0x1010, 0x3232, 0x1010, 0x3232} // Replicate 16-bit values
};
if (mode >= 1 && mode <= 6) {
return byte_level_permute(low32, high32, lookup[mode - 1][sel & 0x3]);
} else if (!mode) {
return byte_level_permute(low32, high32, sel);
}
return 0;
}
} // COPY from DPCT head files
#endif // GGML_SYCL_DPCT_HELPER_HPP

97
ggml/src/ggml-sycl/element_wise.cpp
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@ -911,6 +911,98 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
});
}
__dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
x = sycl::fmin(x, limit);
g = sycl::fmax(sycl::fmin(g, limit), -limit);
float out_glu = x / (1.0f + sycl::native::exp(-x * alpha));
out_glu = out_glu * (1.0f + g);
return out_glu;
}
template <typename T>
static void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k,
const int64_t n, const int64_t o0, const int64_t o1,
float alpha, float limit, sycl::nd_item<3> item_ct1) {
const int64_t i = int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2);
if (i >= k) {
return;
}
const int64_t j0 = (i / n) * o0 + (i % n);
const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
float xi = x[j0];
float gi = g[j1];
dst[i] = ggml_sycl_op_swiglu_oai_single(xi, gi, alpha, limit);
}
template <typename T>
static void swiglu_oai_sycl(const T * x,
const T * g,
T * dst,
const int64_t k,
const int64_t n,
const int64_t o0,
const int64_t o1,
const float alpha,
const float limit,
dpct::queue_ptr stream) {
const int64_t num_blocks = (k + SYCL_GLU_BLOCK_SIZE - 1) / SYCL_GLU_BLOCK_SIZE;
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE),
sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
swiglu_oai_kernel(x, g, dst, k, n, o0, o1, alpha, limit, item_ct1);
});
}
void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
void * src0_d = src0->data;
void * src1_d = src1 ? src1->data : src0->data;
const int64_t src0_o = src0->nb[1];
const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
void * dst_d = dst->data;
const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
dpct::queue_ptr stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous_1(src0));
GGML_ASSERT(src0->nb[0] == ggml_element_size(src0));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->type == dst->type);
GGML_ASSERT(dst->ne[0] == nc);
GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0));
if (src1) {
GGML_ASSERT(ggml_is_contiguous_1(src1));
GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
GGML_ASSERT(src1->ne[0] == nc);
GGML_ASSERT(src0->type == src1->type);
}
//const int32_t swapped = ((const int32_t *) dst->op_params)[1];
const int32_t swapped = ggml_get_op_params_i32(dst, 1);
const float alpha = ggml_get_op_params_f32(dst, 2);
const float limit = ggml_get_op_params_f32(dst, 3);
float * src0_p = (float *) src0_d;
float * src1_p = (float *) src1_d;
if (!src1) {
src0_p += swapped ? nc : 0;
src1_p += swapped ? 0 : nc;
}
swiglu_oai_sycl(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
}
static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
[](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
@ -1070,6 +1162,11 @@ void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
ggml_sycl_op_swiglu(ctx, dst);
}
void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
ggml_sycl_op_swiglu_oai(ctx, dst);
}
void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
ggml_sycl_op_geglu_erf(ctx, dst);

4
ggml/src/ggml-sycl/element_wise.hpp
View File

@ -5,6 +5,8 @@
#include "ggml.h"
#include <limits> // For std::numeric_limits
#define SYCL_GLU_BLOCK_SIZE 256
template <typename T>
T neg_infinity() {
return -std::numeric_limits<T>::infinity();
@ -41,6 +43,8 @@ void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

21
ggml/src/ggml-sycl/ggml-sycl.cpp
View File

@ -39,6 +39,7 @@
#include "ggml-impl.h"
#include "ggml-backend-impl.h"
#include "ggml-sycl/add-id.hpp"
#include "ggml-sycl/backend.hpp"
#include "ggml-sycl/common.hpp"
#include "ggml-sycl/element_wise.hpp"
@ -3313,6 +3314,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
// mmvq and mmq need the __dp4a instruction which is available for gen12+
// Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
@ -3320,7 +3322,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
#endif // SYCL_USE_XMX
// mmvq path is faster in the CUDA backend.
if (!g_ggml_sycl_prioritize_dmmv && (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda
// Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization
@ -3711,6 +3712,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
case GGML_OP_ADD1: // TODO: more efficient implementation
ggml_sycl_add(ctx, dst);
break;
case GGML_OP_ADD_ID:
ggml_sycl_add_id(ctx, dst);
break;
case GGML_OP_SUB:
ggml_sycl_sub(ctx, dst);
break;
@ -3803,6 +3807,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
case GGML_GLU_OP_SWIGLU:
ggml_sycl_swiglu(ctx, dst);
break;
case GGML_GLU_OP_SWIGLU_OAI:
ggml_sycl_swiglu_oai(ctx, dst);
break;
case GGML_GLU_OP_GEGLU_ERF:
ggml_sycl_geglu_erf(ctx, dst);
break;
@ -4397,6 +4404,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_GLU_OP_REGLU:
case GGML_GLU_OP_GEGLU:
case GGML_GLU_OP_SWIGLU:
case GGML_GLU_OP_SWIGLU_OAI:
case GGML_GLU_OP_GEGLU_ERF:
case GGML_GLU_OP_GEGLU_QUICK:
return ggml_is_contiguous_1(op->src[0]);
@ -4424,15 +4432,18 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
}
}
ggml_type src0_type = op->src[0]->type;
if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) {
// TODO: support MXFP4
if (src0_type == GGML_TYPE_BF16 ) {
// TODO: support GGML_TYPE_BF16
// FIXME: keep a list of supported types to avoid breaking the backend when a new type is added
return false;
}
// TODO: The configuration below needs more work to be supported with oneDNN
if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && a->ne[2] > 1 && a->ne[3] > 1) {
if (ggml_is_permuted(a) && !ggml_is_contiguous(a) &&
a->ne[2] > 1 && a->ne[3] > 1 && src0_type == GGML_TYPE_F16) {
return false;
}
// TODO: This specific configuration can fail with oneDNN and needs more debugging
if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 &&
a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) {
@ -4553,9 +4564,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_ADD_ID:
case GGML_OP_SUB:
case GGML_OP_COUNT_EQUAL:
case GGML_OP_MUL:

22
ggml/src/ggml-sycl/mmvq.cpp
View File

@ -595,6 +595,25 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
}
}
static void mul_mat_vec_mxfp4_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols, const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_MXFP4 == 0);
const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
{
stream->submit([&](sycl::handler & cgh) {
cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_MXFP4, QI_MXFP4, block_mxfp4, VDR_MXFP4_Q8_1_MMVQ, vec_dot_mxfp4_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
}
}
static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
float *dst, const int ncols,
const int nrows,
@ -1123,6 +1142,9 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
case GGML_TYPE_IQ4_XS:
mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_MXFP4:
mul_mat_vec_mxfp4_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
default:
GGML_ABORT("fatal error");
}

6
ggml/src/ggml-sycl/pad.cpp
View File

@ -16,8 +16,8 @@
static void pad_f32(const float * src, float * dst,
const int lp0, const int rp0, const int lp1, const int rp1,
const int lp2, const int rp2, const int lp3, const int rp3,
const int ne0, const int ne1, const int ne2, const int ne3) {
auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
const int ne0, const int ne1, const int ne2, const int ne3,
sycl::nd_item<3> item_ct1) {
int i0 = item_ct1.get_local_id(2) +
item_ct1.get_group(2) * item_ct1.get_local_range(2);
int i1 = item_ct1.get_group(1);
@ -63,7 +63,7 @@ static void pad_f32_sycl(const float *src, float *dst, const int lp0,
sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
[=](sycl::nd_item<3> item_ct1) {
pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1,
ne2, ne3);
ne2, ne3, item_ct1);
});
}

2
ggml/src/ggml-sycl/ssm_conv.cpp
View File

@ -88,7 +88,7 @@ void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT(src1->nb[0] == sizeof(float));
GGML_ASSERT(src0->nb[1] == src0->ne[0] * static_cast<int>(sizeof(float)));
GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));
const int src_stride_inner = ncs;
const int src_stride_seq = ncs * d_inner;

58
ggml/src/ggml-sycl/vecdotq.hpp
View File

@ -20,6 +20,18 @@
typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1,
const int & iqs);
static __dpct_inline__ int get_int_b1(const void * x, const int & i32) {
const uint8_t * x8 = (const uint8_t *) x;
int x32 = x8[4*i32 + 0] << 0;
x32 |= x8[4*i32 + 1] << 8;
x32 |= x8[4*i32 + 2] << 16;
x32 |= x8[4*i32 + 3] << 24;
return x32;
}
static __dpct_inline__ int get_int_from_int8(const int8_t* x8, const int& i32) {
const uint16_t* x16 =
(const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte
@ -75,6 +87,28 @@ static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4,
val2 = v1 | (v2 << 16);
}
static __dpct_inline__ sycl::int2 get_int_from_table_16(
const int& q4, const int8_t* table) {
const uint32_t* table32 = (const uint32_t*)table;
uint32_t tmp[2];
const uint32_t low_high_selection_indices =
(0x32103210 | ((q4 & 0x88888888) >> 1));
#pragma unroll
for (uint32_t i = 0; i < 2; ++i) {
const uint32_t shift = 16 * i;
const uint32_t low =
dpct::byte_level_permute(table32[0], table32[1], q4 >> shift);
const uint32_t high =
dpct::byte_level_permute(table32[2], table32[3], q4 >> shift);
tmp[i] = dpct::byte_level_permute(
low, high, low_high_selection_indices >> shift);
}
return sycl::int2(
dpct::byte_level_permute(tmp[0], tmp[1], 0x6420),
dpct::byte_level_permute(tmp[0], tmp[1], 0x7531));
}
#define VDR_Q2_K_Q8_1_MMVQ 1
// contiguous v/x values
@ -685,6 +719,30 @@ vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
}
#define VDR_MXFP4_Q8_1_MMVQ 2
#define VDR_MXFP4_Q8_1_MMQ 4
static __dpct_inline__ float vec_dot_mxfp4_q8_1(const void * __restrict__ vbq,
const block_q8_1 * __restrict__ bq8_1,
const int & iqs) {
const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq;
const int * q8 = (const int *) bq8_1->qs + iqs;
int sumi = 0;
#pragma unroll
for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
const sycl::int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
sumi = ggml_sycl_dp4a(v.x(), q8[l + 0], sumi);
sumi = ggml_sycl_dp4a(v.y(), q8[l + 4], sumi);
}
const float d = ggml_sycl_e8m0_to_fp32(bq4->e) * 0.5f * (bq8_1->ds)[0];
return d * sumi;
}
static __dpct_inline__ float
vec_dot_q5_0_q8_1(const void *__restrict__ vbq,
const block_q8_1 *__restrict__ bq8_1, const int &iqs) {