mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
ggml : sync (abort callback, mul / add broadcast, fix alibi) (#2183)
This commit is contained in:
Georgi Gerganov
GitHub
parent
5bf2a27718
commit
20d7740a9b
115
ggml-cuda.cu
115
ggml-cuda.cu
@ -239,13 +239,13 @@ struct ggml_tensor_extra_gpu {
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cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs
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};
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static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) {
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static __global__ void add_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= k) {
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if (i >= kx) {
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return;
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}
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dst[i] = x[i] + y[i];
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dst[i] = x[i] + y[i%ky];
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}
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static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) {
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@ -275,16 +275,46 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) {
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dst[i] = x[i] / (1.0f + expf(-x[i]));
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}
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static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
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const int row = blockIdx.x*blockDim.y + threadIdx.y;
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const int tid = threadIdx.x;
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const float eps = 1e-5f;
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float mean = 0.0f;
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float var = 0.0f;
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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const float xi = x[row*ncols + col];
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mean += xi;
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var += xi * xi;
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}
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// sum up partial sums
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#pragma unroll
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for (int mask = 16; mask > 0; mask >>= 1) {
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mean += __shfl_xor_sync(0xffffffff, mean, mask, 32);
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var += __shfl_xor_sync(0xffffffff, var, mask, 32);
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}
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mean /= ncols;
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var = var / ncols - mean * mean;
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const float inv_var = rsqrtf(var + eps);
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var;
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}
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}
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static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols) {
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const int row = blockIdx.x*blockDim.y + threadIdx.y;
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const int tid = threadIdx.x;
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const float eps = 1e-6;
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const float eps = 1e-6f;
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float tmp = 0.0f; // partial sum for thread in warp
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for (int i = 0; i < ncols; i += WARP_SIZE) {
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const int col = i + tid;
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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const float xi = x[row*ncols + col];
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tmp += xi * xi;
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}
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@ -296,10 +326,9 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
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}
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const float mean = tmp / ncols;
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const float scale = 1.0f / sqrtf(mean + eps);
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const float scale = rsqrtf(mean + eps);
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for (int i = 0; i < ncols; i += WARP_SIZE) {
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const int col = i + tid;
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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dst[row*ncols + col] = scale * x[row*ncols + col];
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}
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}
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@ -1689,9 +1718,9 @@ static __global__ void scale_f32(const float * x, float * dst, const float scale
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dst[i] = scale * x[i];
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}
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static void add_f32_cuda(const float * x, const float * y, float * dst, const int k, cudaStream_t stream) {
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const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
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add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
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static void add_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
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const int num_blocks = (kx + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
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add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
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}
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static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) {
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@ -1709,6 +1738,12 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_
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silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
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}
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static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % WARP_SIZE == 0);
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const dim3 block_dims(WARP_SIZE, 1, 1);
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norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
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}
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static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
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GGML_ASSERT(ncols % WARP_SIZE == 0);
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const dim3 block_dims(WARP_SIZE, 1, 1);
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@ -2239,14 +2274,16 @@ inline void ggml_cuda_op_add(
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GGML_ASSERT(src1_ddf_i != nullptr);
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GGML_ASSERT(dst_ddf_i != nullptr);
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const int64_t ne0 = src0->ne[0];
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const int64_t ne00 = src0->ne[0];
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const int64_t i01_diff = i01_high - i01_low;
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const int64_t ne10 = src1->ne[0];
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// compute
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if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
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add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main);
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add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne00*i01_diff, ne10, cudaStream_main);
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} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
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add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne0*i01_diff, cudaStream_main);
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add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne00*i01_diff, cudaStream_main);
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} else {
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GGML_ASSERT(false);
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}
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@ -2268,20 +2305,11 @@ inline void ggml_cuda_op_mul(
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GGML_ASSERT(dst_ddf_i != nullptr);
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const int64_t ne00 = src0->ne[0];
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const int64_t i01_diff = i01_high - i01_low;
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const int64_t ne10 = src1->ne[0];
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const int64_t ne11 = src1->ne[1];
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for (int64_t i01 = i01_low; i01 < i01_high; i01++) {
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const int64_t i11 = i1*ne11 + i01%ne11; // broadcast src1 across src0
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float * src0_ddf_i01 = src0_ddf_i + i01*ne00;
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float * src1_ddf_i01 = src1_ddf_i + i11*ne10;
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float * dst_ddf_i01 = dst_ddf_i + i01*ne00;
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// compute
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mul_f32_cuda(src0_ddf_i01, src1_ddf_i01, dst_ddf_i01, ne00, ne10, cudaStream_main);
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}
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mul_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne00*i01_diff, ne10, cudaStream_main);
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(void) dst;
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(void) src0_ddq_i;
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@ -2310,6 +2338,28 @@ inline void ggml_cuda_op_silu(
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(void) i1;
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}
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inline void ggml_cuda_op_norm(
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const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
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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,
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cudaStream_t & cudaStream_main){
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GGML_ASSERT(src0_ddf_i != nullptr);
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GGML_ASSERT(dst_ddf_i != nullptr);
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const int64_t ne00 = src0->ne[0];
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const int64_t i01_diff = i01_high - i01_low;
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// compute
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norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
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(void) src1;
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(void) dst;
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(void) src0_ddq_i;
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(void) src1_ddf_i;
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(void) i02;
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(void) i1;
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}
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inline void ggml_cuda_op_rms_norm(
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const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
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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,
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@ -2930,6 +2980,11 @@ void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_ten
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ggml_cuda_op(src0, src1, dst, ggml_cuda_op_silu, true, true);
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}
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void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
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ggml_cuda_op(src0, src1, dst, ggml_cuda_op_norm, true, true);
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}
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void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
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ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rms_norm, true, true);
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@ -3160,7 +3215,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
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}
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cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
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CUDA_CHECK(cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice));
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extra->data_device[id] = buf;
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@ -3322,6 +3377,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
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}
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func = ggml_cuda_silu;
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break;
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case GGML_OP_NORM:
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if (!any_on_device) {
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return false;
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}
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func = ggml_cuda_norm;
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break;
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case GGML_OP_RMS_NORM:
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if (!any_on_device) {
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return false;
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115
ggml.c
115
ggml.c
@ -25,6 +25,7 @@
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#include <float.h>
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#include <limits.h>
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#include <stdarg.h>
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#include <signal.h>
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#ifdef GGML_USE_METAL
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#include <unistd.h>
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@ -49,23 +50,23 @@
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typedef volatile LONG atomic_int;
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typedef atomic_int atomic_bool;
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static void atomic_store(atomic_int* ptr, LONG val) {
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static void atomic_store(atomic_int * ptr, LONG val) {
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InterlockedExchange(ptr, val);
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}
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static LONG atomic_load(atomic_int* ptr) {
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static LONG atomic_load(atomic_int * ptr) {
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return InterlockedCompareExchange(ptr, 0, 0);
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}
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static LONG atomic_fetch_add(atomic_int* ptr, LONG inc) {
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static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) {
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return InterlockedExchangeAdd(ptr, inc);
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}
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static LONG atomic_fetch_sub(atomic_int* ptr, LONG dec) {
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static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) {
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return atomic_fetch_add(ptr, -(dec));
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}
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typedef HANDLE pthread_t;
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typedef DWORD thread_ret_t;
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static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) {
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static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) {
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(void) unused;
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HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
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if (handle == NULL)
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@ -77,7 +78,7 @@ static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void
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return 0;
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}
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static int pthread_join(pthread_t thread, void* unused) {
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static int pthread_join(pthread_t thread, void * unused) {
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(void) unused;
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return (int) WaitForSingleObject(thread, INFINITE);
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}
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@ -90,7 +91,7 @@ static int sched_yield (void) {
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#include <pthread.h>
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#include <stdatomic.h>
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typedef void* thread_ret_t;
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typedef void * thread_ret_t;
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#include <sys/types.h>
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#include <sys/stat.h>
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@ -4723,7 +4724,7 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) {
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{
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assert(tensor->nb[0] == sizeof(ggml_fp16_t));
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for (int i = 0; i < n; i++) {
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ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value);
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ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
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}
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} break;
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case GGML_TYPE_F32:
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@ -4775,7 +4776,7 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) {
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{
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assert(tensor->nb[0] == sizeof(ggml_fp16_t));
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for (int i = 0; i < n; i++) {
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ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), value);
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ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value));
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}
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} break;
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case GGML_TYPE_F32:
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@ -5035,11 +5036,15 @@ struct ggml_tensor * ggml_add_impl(
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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bool inplace) {
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GGML_ASSERT(ggml_are_same_shape(a, b));
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// TODO: support less-strict constraint
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// GGML_ASSERT(ggml_can_repeat(b, a));
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GGML_ASSERT(ggml_can_repeat_rows(b, a));
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bool is_node = false;
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if (a->grad || b->grad) {
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if (!inplace && (a->grad || b->grad)) {
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// TODO: support backward pass for broadcasting
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GGML_ASSERT(ggml_are_same_shape(a, b));
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is_node = true;
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}
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@ -8297,7 +8302,7 @@ static void ggml_compute_forward_add_f32(
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
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GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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@ -8322,23 +8327,23 @@ static void ggml_compute_forward_add_f32(
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if (nb10 == sizeof(float)) {
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for (int ir = ir0; ir < ir1; ++ir) {
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// src0, src1 and dst are same shape => same indices
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const int i3 = ir/(ne2*ne1);
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const int i2 = (ir - i3*ne2*ne1)/ne1;
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const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
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// src1 is broadcastable across src0 and dst in i1, i2, i3
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const int64_t i03 = ir/(ne02*ne01);
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const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
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const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
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const int64_t i13 = i03 % ne13;
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const int64_t i12 = i02 % ne12;
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const int64_t i11 = i01 % ne11;
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float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
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float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
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float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
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#ifdef GGML_USE_ACCELERATE
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vDSP_vadd(
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(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
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(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
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(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1,
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ne0);
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vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
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#else
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ggml_vec_add_f32(ne0,
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(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ),
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(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
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(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
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ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
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#endif
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// }
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// }
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@ -8346,15 +8351,20 @@ static void ggml_compute_forward_add_f32(
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} else {
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// src1 is not contiguous
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for (int ir = ir0; ir < ir1; ++ir) {
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// 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) {
|
||||
|
||||
11
ggml.h
11
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
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -7,7 +7,9 @@
|
||||
|
||||
#define MAX_NARGS 2
|
||||
|
||||
#if defined(__GNUC__)
|
||||
#pragma GCC diagnostic ignored "-Wdouble-promotion"
|
||||
#endif
|
||||
|
||||
//
|
||||
// logging
|
||||
|
||||
Loading…
Reference in New Issue
Block a user