EngineX-Ascend/enginex-ascend-910-llama.cpp
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[SYCL] refactor soft_max, add soft_max_back (#16472)

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* refactor to support soft_max_ext

* fix error and support soft_max_back

* rm unused functions

* fix format issue

---------

Co-authored-by: Zhang Jianyu <zhang.jianyu@outlook.com>
This commit is contained in:
Neo Zhang Jianyu
2025-10-09 15:25:11 +08:00
committed by GitHub
parent e08db42595
commit b260213755
5 changed files with 437 additions and 189 deletions
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491
ggml/src/ggml-sycl/softmax.cpp
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@@ -1,37 +1,94 @@
#include "softmax.hpp"
#include <cstdint>
#include <utility>
#include <cmath>
template <bool vals_smem, int ncols_template, int block_size_template, typename T>
static void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par,
const int nrows_y, const float scale, const float max_bias, const float m0,
const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
const int tid = item_ct1.get_local_id(2);
const int rowx = item_ct1.get_group(2);
const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
template <typename T> static __dpct_inline__ float t2f32(T val) {
return (float) val;
}
const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
template <> float __dpct_inline__ t2f32<sycl::half>(sycl::half val) {
return sycl::vec<sycl::half, 1>(val)
.convert<float, sycl::rounding_mode::automatic>()[0];
}
const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
struct soft_max_params {
int64_t nheads;
uint32_t n_head_log2;
int64_t ncols;
int64_t nrows_x;
int64_t nrows_y;
int64_t ne00;
int64_t ne01;
int64_t ne02;
int64_t ne03;
int64_t nb11;
int64_t nb12;
int64_t nb13;
int64_t ne12;
int64_t ne13;
float scale;
float max_bias;
float m0;
float m1;
};
// When ncols_template == 0 the bounds for the loops in this function are not known and can't be unrolled.
// As we want to keep pragma unroll for all other cases we supress the clang transformation warning here.
#ifdef __clang__
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wpass-failed"
#endif // __clang__
template <bool use_shared, int ncols_template, int block_size_template, typename T>
static void soft_max_f32(const float * x,
const T * mask,
const float * sinks,
float * dst,
const soft_max_params p,
uint8_t * dpct_local) {
auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
const int ncols = ncols_template == 0 ? p.ncols : ncols_template;
const int block_size = block_size_template == 0
? item_ct1.get_local_range(2)
: block_size_template;
const int nthreads = block_size;
const int nwarps = nthreads / WARP_SIZE;
size_t nreduce = nwarps / WARP_SIZE;
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
const uint32_t h = rowx/nrows_y; // head index
const int tid = item_ct1.get_local_id(2);
const float base = h < n_head_log2 ? m0 : m1;
const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
const int64_t i03 = item_ct1.get_group(0);
const int64_t i02 = item_ct1.get_group(1);
const int64_t i01 = item_ct1.get_group(2);
slope = sycl::pow(base, float(exp));
}
//TODO: noncontigous inputs/outputs
const int rowx = item_ct1.get_group(2) +
item_ct1.get_group(1) * item_ct1.get_group_range(2) +
item_ct1.get_group(0) * item_ct1.get_group_range(2) *
item_ct1.get_group_range(1);
float *vals = vals_smem ? buf + sycl::max(nwarps, WARP_SIZE) : dst + rowx * ncols;
float max_val = -INFINITY;
const int64_t i11 = i01;
const int64_t i12 = i02 % p.ne12;
const int64_t i13 = i03 % p.ne13;
x += int64_t(rowx)*ncols;
mask += (i11*p.nb11 + i12*p.nb12 + i13*p.nb13) / sizeof(T) * (mask != nullptr);
dst += int64_t(rowx)*ncols;
const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
const float slope = get_alibi_slope(p.max_bias, i02, p.n_head_log2, p.m0, p.m1);
float * buf_iw = (float *) dpct_local;
// shared memory buffer to cache values between iterations:
float *vals = use_shared ? buf_iw + sycl::max(nwarps, WARP_SIZE) : dst;
float max_val = sinks ? sinks[i02] : -INFINITY;
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
@@ -39,42 +96,35 @@ static void soft_max_f32(const float * x, const T * mask, float * dst, const int
break;
}
const int ix = rowx*ncols + col;
const int iy = rowy*ncols + col;
const float val = x[ix]*scale + (mask ? slope*static_cast<float>(mask[iy]) : 0.0f);
const float val = x[col]*p.scale + (mask ? slope*t2f32(mask[col]) : 0.0f);
vals[col] = val;
max_val = sycl::max(max_val, val);
max_val = sycl::max(max_val, val);
}
// find the max value in the block
max_val = warp_reduce_max(max_val, item_ct1);
max_val = warp_reduce_max(max_val);
if (block_size > WARP_SIZE) {
if (warp_id == 0) {
buf[lane_id] = -INFINITY;
for (size_t i = 1; i < nreduce; i += 1) {
buf[lane_id + i * WARP_SIZE] = -INFINITY;
}
buf_iw[lane_id] = -INFINITY;
}
item_ct1.barrier(sycl::access::fence_space::local_space);
item_ct1.barrier();
if (lane_id == 0) {
buf[warp_id] = max_val;
buf_iw[warp_id] = max_val;
}
item_ct1.barrier(sycl::access::fence_space::local_space);
max_val = buf[lane_id];
for (size_t i = 1; i < nreduce; i += 1) {
max_val = sycl::max(max_val, buf[lane_id + i * WARP_SIZE]);
}
max_val = warp_reduce_max(max_val, item_ct1);
}
item_ct1.barrier();
max_val = buf_iw[lane_id];
max_val = warp_reduce_max(max_val);
}
float tmp = 0.0f; // partial sum
float tmp = 0.f;
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
if (ncols_template == 0 && col >= ncols) {
break;
}
@@ -82,32 +132,33 @@ static void soft_max_f32(const float * x, const T * mask, float * dst, const int
tmp += val;
vals[col] = val;
}
// find the sum of exps in the block
tmp = warp_reduce_sum(tmp, item_ct1);
tmp = warp_reduce_sum(tmp);
if (block_size > WARP_SIZE) {
item_ct1.barrier(sycl::access::fence_space::local_space);
item_ct1.barrier();
if (warp_id == 0) {
buf[lane_id] = 0.f;
buf_iw[lane_id] = 0.0f;
for (size_t i = 1; i < nreduce; i += 1) {
buf[lane_id + i * WARP_SIZE] = 0.f;
buf_iw[lane_id + i * WARP_SIZE] = 0.f;
}
}
item_ct1.barrier(sycl::access::fence_space::local_space);
item_ct1.barrier();
if (lane_id == 0) {
buf[warp_id] = tmp;
buf_iw[warp_id] = tmp;
}
item_ct1.barrier(sycl::access::fence_space::local_space);
item_ct1.barrier();
tmp = buf[lane_id];
tmp = buf_iw[lane_id];
for (size_t i = 1; i < nreduce; i += 1) {
tmp += buf[lane_id + i * WARP_SIZE];
tmp += buf_iw[lane_id + i * WARP_SIZE];
}
tmp = warp_reduce_sum(tmp, item_ct1);
tmp = warp_reduce_sum(tmp);
}
const float inv_sum = 1.f / tmp;
if (sinks) {
tmp += sycl::native::exp(sinks[i02] - max_val);
}
const float inv_sum = 1.0f / tmp;
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
@@ -117,145 +168,259 @@ static void soft_max_f32(const float * x, const T * mask, float * dst, const int
return;
}
const int idst = rowx*ncols + col;
dst[idst] = vals[col] * inv_sum;
dst[col] = vals[col] * inv_sum;
}
}
#ifdef __clang__
#pragma clang diagnostic pop
#endif // __clang__
static void soft_max_back_f32(const float *grad, const float *dstf, float *dst,
const int ncols, const float scale) {
auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
const int tid = item_ct1.get_local_id(2);
const int rowx = item_ct1.get_group(2);
grad += int64_t(rowx)*ncols;
dstf += int64_t(rowx)*ncols;
dst += int64_t(rowx)*ncols;
float dgf_dot = 0.0f; // dot product of dst from forward pass and gradients
for (int col = tid; col < ncols; col += WARP_SIZE) {
dgf_dot += dstf[col]*grad[col];
}
dgf_dot = warp_reduce_sum(dgf_dot);
for (int col = tid; col < ncols; col += WARP_SIZE) {
dst[col] = scale * (grad[col] - dgf_dot) * dstf[col];
}
}
template <bool vals_smem, int ncols_template, int block_size_template, typename T>
static void soft_max_f32_submitter(const float * x, const T * mask, float * dst, const int ncols_par,
const int nrows_y, const float scale, const float max_bias, const float m0,
const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
const size_t n_local_scratch, queue_ptr stream) {
template <int... Ns, typename T>
static void launch_soft_max_kernels(const float * x,
const T * mask,
const float * sinks,
float * dst,
const soft_max_params & p,
dpct::queue_ptr stream,
dpct::dim3 block_dims,
dpct::dim3 block_nums,
size_t nbytes_shared)
{
auto launch_kernel = [=](auto I) -> bool {
constexpr int ncols = decltype(I)::value;
constexpr int block = (ncols > 1024 ? 1024 : ncols);
if (p.ncols == ncols) {
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
sycl::range<1>(nbytes_shared), 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)]] {
soft_max_f32<true, ncols, block>(
x, mask, sinks, dst, p,
dpct_local_acc_ct1
.get_multi_ptr<sycl::access::decorated::no>()
.get());
GGML_UNUSED(item_ct1);
});
});
return true;
}
return false;
};
// unary fold over launch_kernel
if ((launch_kernel(std::integral_constant<int, Ns>{}) || ...)) {
return;
}
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
sycl::range<1>(nbytes_shared), 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)]] {
soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
nrows_y, scale, max_bias, m0,
m1, n_head_log2, item_ct1,
get_pointer(local_buf_acc));
});
[=](sycl::nd_item<3> item_ct1)
[[sycl::reqd_sub_group_size(WARP_SIZE)]] {
soft_max_f32<true, 0, 0>(
x, mask, sinks, dst, p,
dpct_local_acc_ct1
.get_multi_ptr<sycl::access::decorated::no>()
.get());
GGML_UNUSED(item_ct1);
});
});
}
template<typename T>
static void soft_max_f32_sycl(const float * x, const T * mask,
float * dst, const int ncols_x, const int nrows_x,
const int nrows_y, const float scale, const float max_bias,
queue_ptr stream, int device) {
template <typename T>
static void soft_max_f32_sycl(const float *x, const T *mask,
const float *sinks, float *dst,
const soft_max_params &params,
dpct::queue_ptr stream, int device) {
int nth = WARP_SIZE;
int max_block_size = ggml_sycl_info().max_work_group_sizes[device];
const int64_t ncols_x = params.ncols;
while (nth < ncols_x && nth < max_block_size) nth *= 2;
if (nth>max_block_size) nth = max_block_size;
const sycl::range<3> block_dims(1, 1, nth);
const sycl::range<3> block_nums(1, 1, nrows_x);
const size_t n_val_tmp = nth / WARP_SIZE;
const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + n_val_tmp);
const dpct::dim3 block_dims(nth, 1, 1);
const dpct::dim3 block_nums(params.ne01, params.ne02, params.ne03);
const size_t nbytes_shared =
(GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE) * sizeof(float);
const uint32_t n_head_kv = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
const int id = get_current_device_id();
const size_t smpbo = ggml_sycl_info().devices[id].smpbo;
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
if (n_local_scratch*sizeof(float) < local_mem_size) {
if (ncols_x > max_block_size) {
soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
return;
}
switch (ncols_x) {
case 32:
soft_max_f32_submitter<true, 32, 32>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 64:
soft_max_f32_submitter<true, 64, 64>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 128:
soft_max_f32_submitter<true, 128, 128>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 256:
soft_max_f32_submitter<true, 256, 256>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 512:
soft_max_f32_submitter<true, 512, 512>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 1024:
soft_max_f32_submitter<true, 1024, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 2048:
soft_max_f32_submitter<true, 2048, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 4096:
soft_max_f32_submitter<true, 4096, 1024>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
default:
soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
}
if (nbytes_shared <= smpbo) {
launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(
x, mask, sinks, dst, params, stream, block_dims, block_nums,
nbytes_shared);
} else {
soft_max_f32_submitter<false, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, WARP_SIZE, stream);
const size_t nbytes_shared_low = WARP_SIZE * sizeof(float);
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
sycl::range<1>(nbytes_shared_low), cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
soft_max_f32<false, 0, 0>(
x, mask, sinks, dst, params,
dpct_local_acc_ct1
.get_multi_ptr<sycl::access::decorated::no>()
.get());
GGML_UNUSED(item_ct1);
});
});
}
}
static void soft_max_back_f32_sycl(const float * grad,
const float * dstf,
float * dst,
const int ncols,
const int nrows,
const float scale,
dpct::queue_ptr stream) {
const dpct::dim3 block_dims(WARP_SIZE, 1, 1);
const dpct::dim3 block_nums(nrows, 1, 1);
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
soft_max_back_f32(grad, dstf, dst, ncols, scale);
GGML_UNUSED(item_ct1);
});
}
void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const ggml_tensor * src2 = dst->src[2];
const float * src0_d = (const float *) src0->data;
const void * src1_d = src1 ? (const void *) src1->data : nullptr;
const void * src2_d = src2 ? (const void *) src2->data : nullptr;
float * dst_d = (float *) dst->data;
dpct::queue_ptr stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(!dst->src[1] || dst->src[1]->type == GGML_TYPE_F16 || dst->src[1]->type == GGML_TYPE_F32); // src1 contains mask and it is optional
// src1 contains mask and it is optional
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
const int64_t ne00 = dst->src[0]->ne[0];
const int64_t nrows_x = ggml_nrows(dst->src[0]);
const int64_t nrows_y = dst->src[0]->ne[1];
const int64_t nrows_x = ggml_nrows(src0);
const int64_t nrows_y = src0->ne[1];
float scale = 1.0f;
const int64_t ne00 = src0->ne[0];
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, dst->op_params + 0, sizeof(float));
memcpy(&max_bias, dst->op_params + 1, sizeof(float));
memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float));
memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
const float * src0_dd = static_cast<const float *>(dst->src[0]->data);
float * dst_dd = static_cast<float *>(dst->data);
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
ggml_sycl_set_device(ctx.device);
dpct::queue_ptr main_stream = ctx.stream();
const int64_t nb11 = src1 ? src1->nb[1] : 1;
const int64_t nb12 = src1 ? src1->nb[2] : 1;
const int64_t nb13 = src1 ? src1->nb[3] : 1;
if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F16) {
const sycl::half * src1_dd = static_cast<sycl::half *>(dst->src[1]->data);
soft_max_f32_sycl<sycl::half>(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias,
main_stream, ctx.device);
} else if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F32) {
const float * src1_dd = static_cast<const float *>(dst->src[1]->data);
soft_max_f32_sycl<float>(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
const int64_t ne12 = src1 ? src1->ne[2] : 1;
const int64_t ne13 = src1 ? src1->ne[3] : 1;
const uint32_t n_head = src0->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
soft_max_params params = {};
params.nheads = src0->ne[2];
params.n_head_log2 = n_head_log2;
params.ncols = ne00;
params.nrows_x = nrows_x;
params.nrows_y = nrows_y;
params.ne00 = src0->ne[0];
params.ne01 = src0->ne[1];
params.ne02 = src0->ne[2];
params.ne03 = src0->ne[3];
params.nb11 = nb11;
params.nb12 = nb12;
params.nb13 = nb13;
params.ne12 = ne12;
params.ne13 = ne13;
params.scale = scale;
params.max_bias = max_bias;
params.m0 = m0;
params.m1 = m1;
if (use_f16) {
soft_max_f32_sycl(src0_d, (const sycl::half *)src1_d,
(const float *)src2_d, dst_d, params, stream,
ctx.device);
} else {
/* mask unavailable */
soft_max_f32_sycl<float>(src0_dd, nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
soft_max_f32_sycl(src0_d, (const float *)src1_d, (const float *)src2_d,
dst_d, params, stream, ctx.device);
}
}
void ggml_sycl_op_soft_max_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
const ggml_tensor * src0 = dst->src[0]; // grad
const ggml_tensor * src1 = dst->src[1]; // forward pass output
const float * src0_d = (const float *) src0->data;
const float * src1_d = (const float *) src1->data;
float * dst_d = (float *) dst->data;
dpct::queue_ptr stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int64_t ncols = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float));
memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
GGML_ASSERT(max_bias == 0.0f);
soft_max_back_f32_sycl(src0_d, src1_d, dst_d, ncols, nrows, scale, stream);
}