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llama: Add support for RWKV v7 architecture (#12412)

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* ggml: Add op l2_norm

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* ggml: Add op rwkv_wkv7

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: Add support for RWKV7 and ARWKV7 models

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: fix inference with RWKV6Qwen2

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: add more (a)rwkv7 variants in size

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Apply code-format changes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* fix MUSA build

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: fix shape error with rwkv using llama-parallel

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
Molly Sophia
2025-03-18 07:27:50 +08:00
committed by GitHub
parent 60c902926c
commit 7dfad387e3
35 changed files with 2948 additions and 438 deletions
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108
ggml/src/ggml-sycl/norm.cpp
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@@ -180,6 +180,50 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const floa
}
}
static void l2_norm_f32(const float* x, float* dst, const int ncols, const float eps,
const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
item_ct1.get_local_id(1);
const int tid = item_ct1.get_local_id(2);
const int nthreads = item_ct1.get_local_range(2);
const int nwarps = nthreads / WARP_SIZE;
float tmp = 0.0f; // partial sum for thread in warp
for (int col = tid; col < ncols; col += block_size) {
const float xi = x[row * ncols + col];
tmp += xi * xi;
}
// sum up partial sums
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:3: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
item_ct1.barrier(sycl::access::fence_space::local_space);
size_t nreduce = nwarps / WARP_SIZE;
tmp = 0.f;
for (size_t i = 0; i < nreduce; i += 1)
{
tmp += s_sum[lane_id + i * WARP_SIZE];
}
tmp = warp_reduce_sum(tmp, item_ct1);
}
const float scale = sycl::rsqrt(sycl::max(tmp, eps * eps));
for (int col = tid; col < ncols; col += block_size) {
dst[row * ncols + col] = scale * x[row * ncols + col];
}
}
static void norm_f32_sycl(const float* x, float* dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream, int device) {
@@ -311,6 +355,48 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
}
}
static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream, int device) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
// printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
l2_norm_f32(x, dst, ncols, eps, item_ct1,
nullptr, WARP_SIZE);
});
});
}
else {
const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
l2_norm_f32(x, dst, ncols, eps, item_ct1,
get_pointer(s_sum_acc_ct1), work_group_size);
});
});
}
}
void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
ggml_tensor* dst, const float* src0_dd,
const float* src1_dd, float* dst_dd,
@@ -376,3 +462,25 @@ void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* sr
(void)dst;
(void)src1_dd;
}
void ggml_sycl_op_l2_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
const ggml_tensor* src1, ggml_tensor* dst,
const float* src0_dd, const float* src1_dd,
float* dst_dd,
const queue_ptr& main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
l2_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device);
(void)src1;
(void)dst;
(void)src1_dd;
}