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-rw-r--r--ggml-cuda.cu115
-rw-r--r--ggml.c115
-rw-r--r--ggml.h11
-rw-r--r--tests/test-grad0.c2
-rw-r--r--tests/test-opt.c2
5 files changed, 173 insertions, 72 deletions
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 1673e7e..2fb30c6 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -239,13 +239,13 @@ struct ggml_tensor_extra_gpu {
cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs
};
-static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) {
+static __global__ void add_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
- if (i >= k) {
+ if (i >= kx) {
return;
}
- dst[i] = x[i] + y[i];
+ dst[i] = x[i] + y[i%ky];
}
static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) {
@@ -275,16 +275,46 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) {
dst[i] = x[i] / (1.0f + expf(-x[i]));
}
+static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
+ const int row = blockIdx.x*blockDim.y + threadIdx.y;
+ const int tid = threadIdx.x;
+
+ const float eps = 1e-5f;
+
+ float mean = 0.0f;
+ float var = 0.0f;
+
+ for (int col = tid; col < ncols; col += WARP_SIZE) {
+ const float xi = x[row*ncols + col];
+ mean += xi;
+ var += xi * xi;
+ }
+
+ // sum up partial sums
+#pragma unroll
+ for (int mask = 16; mask > 0; mask >>= 1) {
+ mean += __shfl_xor_sync(0xffffffff, mean, mask, 32);
+ var += __shfl_xor_sync(0xffffffff, var, mask, 32);
+ }
+
+ mean /= ncols;
+ var = var / ncols - mean * mean;
+ const float inv_var = rsqrtf(var + eps);
+
+ for (int col = tid; col < ncols; col += WARP_SIZE) {
+ dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var;
+ }
+}
+
static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols) {
const int row = blockIdx.x*blockDim.y + threadIdx.y;
const int tid = threadIdx.x;
- const float eps = 1e-6;
+ const float eps = 1e-6f;
float tmp = 0.0f; // partial sum for thread in warp
- for (int i = 0; i < ncols; i += WARP_SIZE) {
- const int col = i + tid;
+ for (int col = tid; col < ncols; col += WARP_SIZE) {
const float xi = x[row*ncols + col];
tmp += xi * xi;
}
@@ -296,10 +326,9 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
}
const float mean = tmp / ncols;
- const float scale = 1.0f / sqrtf(mean + eps);
+ const float scale = rsqrtf(mean + eps);
- for (int i = 0; i < ncols; i += WARP_SIZE) {
- const int col = i + tid;
+ for (int col = tid; col < ncols; col += WARP_SIZE) {
dst[row*ncols + col] = scale * x[row*ncols + col];
}
}
@@ -1689,9 +1718,9 @@ static __global__ void scale_f32(const float * x, float * dst, const float scale
dst[i] = scale * x[i];
}
-static void add_f32_cuda(const float * x, const float * y, float * dst, const int k, cudaStream_t stream) {
- const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
- add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
+static void add_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
+ const int num_blocks = (kx + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
+ add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
}
static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) {
@@ -1709,6 +1738,12 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_
silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
+static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+ GGML_ASSERT(ncols % WARP_SIZE == 0);
+ const dim3 block_dims(WARP_SIZE, 1, 1);
+ norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
+}
+
static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
const dim3 block_dims(WARP_SIZE, 1, 1);
@@ -2239,14 +2274,16 @@ inline void ggml_cuda_op_add(
GGML_ASSERT(src1_ddf_i != nullptr);
GGML_ASSERT(dst_ddf_i != nullptr);
- const int64_t ne0 = src0->ne[0];
+ const int64_t ne00 = src0->ne[0];
const int64_t i01_diff = i01_high - i01_low;
+ const int64_t ne10 = src1->ne[0];
+
// compute
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
- add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main);
+ add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne00*i01_diff, ne10, cudaStream_main);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
- add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne0*i01_diff, cudaStream_main);
+ add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne00*i01_diff, cudaStream_main);
} else {
GGML_ASSERT(false);
}
@@ -2268,20 +2305,11 @@ inline void ggml_cuda_op_mul(
GGML_ASSERT(dst_ddf_i != nullptr);
const int64_t ne00 = src0->ne[0];
+ const int64_t i01_diff = i01_high - i01_low;
const int64_t ne10 = src1->ne[0];
- const int64_t ne11 = src1->ne[1];
-
- for (int64_t i01 = i01_low; i01 < i01_high; i01++) {
- const int64_t i11 = i1*ne11 + i01%ne11; // broadcast src1 across src0
- float * src0_ddf_i01 = src0_ddf_i + i01*ne00;
- float * src1_ddf_i01 = src1_ddf_i + i11*ne10;
- float * dst_ddf_i01 = dst_ddf_i + i01*ne00;
-
- // compute
- mul_f32_cuda(src0_ddf_i01, src1_ddf_i01, dst_ddf_i01, ne00, ne10, cudaStream_main);
- }
+ mul_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne00*i01_diff, ne10, cudaStream_main);
(void) dst;
(void) src0_ddq_i;
@@ -2310,6 +2338,28 @@ inline void ggml_cuda_op_silu(
(void) i1;
}
+inline void ggml_cuda_op_norm(
+ const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
+ float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
+ cudaStream_t & cudaStream_main){
+
+ GGML_ASSERT(src0_ddf_i != nullptr);
+ GGML_ASSERT(dst_ddf_i != nullptr);
+
+ const int64_t ne00 = src0->ne[0];
+ const int64_t i01_diff = i01_high - i01_low;
+
+ // compute
+ norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
+
+ (void) src1;
+ (void) dst;
+ (void) src0_ddq_i;
+ (void) src1_ddf_i;
+ (void) i02;
+ (void) i1;
+}
+
inline void ggml_cuda_op_rms_norm(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
@@ -2930,6 +2980,11 @@ void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_ten
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_silu, true, true);
}
+void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+ GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+ ggml_cuda_op(src0, src1, dst, ggml_cuda_op_norm, true, true);
+}
+
void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rms_norm, true, true);
@@ -3160,7 +3215,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
}
- cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
+ CUDA_CHECK(cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice));
extra->data_device[id] = buf;
@@ -3322,6 +3377,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
}
func = ggml_cuda_silu;
break;
+ case GGML_OP_NORM:
+ if (!any_on_device) {
+ return false;
+ }
+ func = ggml_cuda_norm;
+ break;
case GGML_OP_RMS_NORM:
if (!any_on_device) {
return false;
diff --git a/ggml.c b/ggml.c
index 8dc30a3..793ff70 100644
--- a/ggml.c
+++ b/ggml.c
@@ -25,6 +25,7 @@
#include <float.h>
#include <limits.h>
#include <stdarg.h>
+#include <signal.h>
#ifdef GGML_USE_METAL
#include <unistd.h>
@@ -49,23 +50,23 @@
typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;
-static void atomic_store(atomic_int* ptr, LONG val) {
+static void atomic_store(atomic_int * ptr, LONG val) {
InterlockedExchange(ptr, val);
}
-static LONG atomic_load(atomic_int* ptr) {
+static LONG atomic_load(atomic_int * ptr) {
return InterlockedCompareExchange(ptr, 0, 0);
}
-static LONG atomic_fetch_add(atomic_int* ptr, LONG inc) {
+static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) {
return InterlockedExchangeAdd(ptr, inc);
}
-static LONG atomic_fetch_sub(atomic_int* ptr, LONG dec) {
+static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) {
return atomic_fetch_add(ptr, -(dec));
}
typedef HANDLE pthread_t;
typedef DWORD thread_ret_t;
-static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) {
+static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) {
(void) unused;
HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
if (handle == NULL)
@@ -77,7 +78,7 @@ static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void
return 0;
}
-static int pthread_join(pthread_t thread, void* unused) {
+static int pthread_join(pthread_t thread, void * unused) {
(void) unused;
return (int) WaitForSingleObject(thread, INFINITE);
}
@@ -90,7 +91,7 @@ static int sched_yield (void) {
#include <pthread.h>
#include <stdatomic.h>
-typedef void* thread_ret_t;
+typedef void * thread_ret_t;
#include <sys/types.h>
#include <sys/stat.h>
@@ -4723,7 +4724,7 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
- ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value);
+ ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
}
} break;
case GGML_TYPE_F32:
@@ -4775,7 +4776,7 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
{
assert(tensor->nb[0] == sizeof(ggml_fp16_t));
for (int i = 0; i < n; i++) {
- ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value);
+ ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
}
} break;
case GGML_TYPE_F32:
@@ -5035,11 +5036,15 @@ struct ggml_tensor * ggml_add_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
- GGML_ASSERT(ggml_are_same_shape(a, b));
+ // TODO: support less-strict constraint
+ // GGML_ASSERT(ggml_can_repeat(b, a));
+ GGML_ASSERT(ggml_can_repeat_rows(b, a));
bool is_node = false;
- if (a->grad || b->grad) {
+ if (!inplace && (a->grad || b->grad)) {
+ // TODO: support backward pass for broadcasting
+ GGML_ASSERT(ggml_are_same_shape(a, b));
is_node = true;
}
@@ -8297,7 +8302,7 @@ static void ggml_compute_forward_add_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
- GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+ GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@@ -8322,23 +8327,23 @@ static void ggml_compute_forward_add_f32(
if (nb10 == sizeof(float)) {
for (int ir = ir0; ir < ir1; ++ir) {
- // src0, src1 and dst are same shape => same indices
- const int i3 = ir/(ne2*ne1);
- const int i2 = (ir - i3*ne2*ne1)/ne1;
- const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+ // src1 is broadcastable across src0 and dst in i1, i2, i3
+ const int64_t i03 = ir/(ne02*ne01);
+ const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+ const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+ const int64_t i13 = i03 % ne13;
+ const int64_t i12 = i02 % ne12;
+ const int64_t i11 = i01 % ne11;
+
+ float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
+ float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+ float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
#ifdef GGML_USE_ACCELERATE
- vDSP_vadd(
- (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
- (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
- (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1,
- ne0);
+ vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
#else
- ggml_vec_add_f32(ne0,
- (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ),
- (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
- (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
+ ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
#endif
// }
// }
@@ -8346,15 +8351,20 @@ static void ggml_compute_forward_add_f32(
} else {
// src1 is not contiguous
for (int ir = ir0; ir < ir1; ++ir) {
- // src0, src1 and dst are same shape => same indices
- const int i3 = ir/(ne2*ne1);
- const int i2 = (ir - i3*ne2*ne1)/ne1;
- const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+ // src1 is broadcastable across src0 and dst in i1, i2, i3
+ const int64_t i03 = ir/(ne02*ne01);
+ const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+ const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+ const int64_t i13 = i03 % ne13;
+ const int64_t i12 = i02 % ne12;
+ const int64_t i11 = i01 % ne11;
+
+ float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
+ float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
- float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 );
- float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
- float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
+ float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
dst_ptr[i0] = src0_ptr[i0] + *src1_ptr;
}
@@ -11717,7 +11727,7 @@ static void ggml_compute_forward_alibi_f32(
const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
const int ne1 = src0->ne[1]; // seq_len_without_past
- //const int ne2 = src0->ne[2]; // n_head -> this is k
+ const int ne2 = src0->ne[2]; // n_head -> this is k
//const int ne3 = src0->ne[3]; // 1 -> bsz
const int n = ggml_nrows(src0);
@@ -11728,8 +11738,9 @@ static void ggml_compute_forward_alibi_f32(
const int nb2 = src0->nb[2];
//const int nb3 = src0->nb[3];
- assert(nb0 == sizeof(float));
- assert(ne1 + n_past == ne0); (void) n_past;
+ GGML_ASSERT(nb0 == sizeof(float));
+ GGML_ASSERT(ne1 + n_past == ne0);
+ GGML_ASSERT(n_head == ne2);
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
@@ -11753,7 +11764,7 @@ static void ggml_compute_forward_alibi_f32(
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
}
- pdst[0] = (i-ne0+1) * m_k + src[0];
+ pdst[0] = i * m_k + src[0];
}
}
@@ -11782,7 +11793,7 @@ static void ggml_compute_forward_alibi_f16(
const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
const int ne1 = src0->ne[1]; // seq_len_without_past
- //const int ne2 = src0->ne[2]; // n_head -> this is k
+ const int ne2 = src0->ne[2]; // n_head -> this is k
//const int ne3 = src0->ne[3]; // 1 -> bsz
const int n = ggml_nrows(src0);
@@ -11793,8 +11804,9 @@ static void ggml_compute_forward_alibi_f16(
const int nb2 = src0->nb[2];
//const int nb3 = src0->nb[3];
- assert(nb0 == sizeof(ggml_fp16_t));
- assert(ne1 + n_past == ne0); (void) n_past;
+ GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
+ GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
+ GGML_ASSERT(n_head == ne2);
// add alibi to src0 (KQ_scaled)
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
@@ -11819,7 +11831,7 @@ static void ggml_compute_forward_alibi_f16(
}
// we return F32
- pdst[0] = (i-ne0+1) * m_k + GGML_FP16_TO_FP32(src[0]);
+ pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
}
}
}
@@ -15944,6 +15956,9 @@ struct ggml_compute_state_shared {
// synchronization primitives
atomic_int n_active; // num active threads
atomic_int node_n; // active graph node
+
+ bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
+ void * abort_callback_data;
};
struct ggml_compute_state {
@@ -15975,6 +15990,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
int node_n = -1;
while (true) {
+ if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
+ state->shared->node_n += 1;
+ return (thread_ret_t) GGML_EXIT_ABORTED;
+ }
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
// all other threads are finished and spinning
// do finalize and init here so we don't have synchronize again
@@ -16028,6 +16047,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
} else {
break;
}
+
+ if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
+ break;
+ }
}
atomic_store(&state->shared->n_active, n_threads);
@@ -16061,7 +16084,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
}
}
- return 0;
+ return GGML_EXIT_SUCCESS;
}
struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
@@ -16401,7 +16424,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
return cplan;
}
-void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
+int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
{
GGML_ASSERT(cplan);
GGML_ASSERT(cplan->n_threads > 0);
@@ -16427,6 +16450,8 @@ void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan)
/*.n_threads =*/ n_threads,
/*.n_active =*/ n_threads,
/*.node_n =*/ -1,
+ /*.abort_callback =*/ NULL,
+ /*.abort_callback_data =*/ NULL,
};
struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads);
@@ -16450,12 +16475,12 @@ void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan)
const int64_t perf_start_time_us = ggml_perf_time_us();
// this is a work thread too
- ggml_graph_compute_thread(&workers[0]);
+ int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]);
// don't leave affinity set on the main thread
clear_numa_thread_affinity();
- // join thread pool
+ // join or kill thread pool
if (n_threads > 1) {
for (int j = 1; j < n_threads; j++) {
const int rc = ggml_thread_join(workers[j].thrd, NULL);
@@ -16479,6 +16504,8 @@ void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan)
(double) perf_time_us_cur / 1000.0,
(double) cgraph->perf_time_us / 1000.0 / cgraph->perf_runs);
}
+
+ return compute_status;
}
void ggml_graph_reset(struct ggml_cgraph * cgraph) {
diff --git a/ggml.h b/ggml.h
index d7c9e0f..8fe05d3 100644
--- a/ggml.h
+++ b/ggml.h
@@ -201,8 +201,13 @@
#define GGML_MAX_NAME 48
#define GGML_DEFAULT_N_THREADS 4
+
+#define GGML_EXIT_SUCCESS 0
+#define GGML_EXIT_ABORTED 1
+
#define GGML_UNUSED(x) (void)(x)
+
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
@@ -442,6 +447,10 @@ extern "C" {
// the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
int n_tasks[GGML_MAX_NODES];
+
+ // abort ggml_graph_compute when true
+ bool (*abort_callback)(void * data);
+ void * abort_callback_data;
};
// computation graph
@@ -1303,7 +1312,7 @@ extern "C" {
// ggml_graph_plan() has to be called before ggml_graph_compute()
// when plan.work_size > 0, caller must allocate memory for plan.work_data
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
- GGML_API void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+ GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
// same as ggml_graph_compute() but the work data is allocated as a part of the context
diff --git a/tests/test-grad0.c b/tests/test-grad0.c
index da4001c..01467bc 100644
--- a/tests/test-grad0.c
+++ b/tests/test-grad0.c
@@ -10,7 +10,9 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
+#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdouble-promotion"
+#endif
#define MAX_NARGS 3
diff --git a/tests/test-opt.c b/tests/test-opt.c
index e928a7d..5531814 100644
--- a/tests/test-opt.c
+++ b/tests/test-opt.c
@@ -7,7 +7,9 @@
#define MAX_NARGS 2
+#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdouble-promotion"
+#endif
//
// logging
generated by cgit v1.2.3 (git 2.25.1) at 2024-10-18 15:27:35 +0000