diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 1673e7e4c..2fb30c6e6 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<<>>(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<<>>(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<<>>(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<<>>(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 8dc30a372..793ff7095 100644 --- a/ggml.c +++ b/ggml.c @@ -25,6 +25,7 @@ #include #include #include +#include #ifdef GGML_USE_METAL #include @@ -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 #include -typedef void* thread_ret_t; +typedef void * thread_ret_t; #include #include @@ -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 d7c9e0f0e..8fe05d3a5 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 da4001ce5..01467bc18 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 e928a7df7..5531814c4 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