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https://github.com/ggerganov/llama.cpp.git
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cuda : fix const ptrs warning causing ROCm build issues (#3913)
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d6069051de
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c7743fe1c1
37
ggml-cuda.cu
37
ggml-cuda.cu
@ -7248,7 +7248,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
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__global__ void k_compute_batched_ptrs(
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__global__ void k_compute_batched_ptrs(
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const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
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const half * src0_as_f16, const half * src1_as_f16, half * dst_f16,
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void ** ptrs,
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const void ** ptrs_src, void ** ptrs_dst,
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int ne12, int ne13,
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int ne12, int ne13,
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int ne23,
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int ne23,
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int nb02, int nb03,
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int nb02, int nb03,
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@ -7265,9 +7265,9 @@ __global__ void k_compute_batched_ptrs(
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int i03 = i13 / r3;
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int i03 = i13 / r3;
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int i02 = i12 / r2;
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int i02 = i12 / r2;
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ptrs[0*ne23 + i12 + i13*ne12] = (char *) src0_as_f16 + i02*nb02 + i03*nb03;
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ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
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ptrs[1*ne23 + i12 + i13*ne12] = (char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
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ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
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ptrs[2*ne23 + i12 + i13*ne12] = (char *) dst_f16 + i12* nb2/2 + i13* nb3/2;
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ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2;
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}
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}
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static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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@ -7372,14 +7372,20 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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} else {
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} else {
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// use cublasGemmBatchedEx
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// use cublasGemmBatchedEx
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const int ne23 = ne12*ne13;
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const int ne23 = ne12*ne13;
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// allocate device memory for pointers
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size_t ptrs_s = 0;
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const void ** ptrs_src = nullptr;
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void ** ptrs_as = (void **)ggml_cuda_pool_malloc_async(3*ne23*sizeof(void *), &ptrs_s, id, main_stream);
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void ** ptrs_dst = nullptr;
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size_t ptrs_src_s = 0;
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size_t ptrs_dst_s = 0;
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ptrs_src = (const void **) ggml_cuda_pool_malloc_async(2*ne23*sizeof(void *), &ptrs_src_s, id, main_stream);
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ptrs_dst = ( void **) ggml_cuda_pool_malloc_async(1*ne23*sizeof(void *), &ptrs_dst_s, id, main_stream);
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dim3 block_dims(ne13, ne12);
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dim3 block_dims(ne13, ne12);
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k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
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k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
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src0_as_f16, src1_as_f16, dst_f16,
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src0_as_f16, src1_as_f16, dst_f16,
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ptrs_as,
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ptrs_src, ptrs_dst,
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ne12, ne13,
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ne12, ne13,
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ne23,
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ne23,
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nb02, nb03,
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nb02, nb03,
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@ -7390,15 +7396,18 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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CUBLAS_CHECK(
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CUBLAS_CHECK(
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cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
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cublasGemmBatchedEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
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ne01, ne11, ne10,
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ne01, ne11, ne10,
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&alpha_f16, (const void * const *) (ptrs_as + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
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&alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
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(const void * const *) (ptrs_as + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
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(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
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&beta_f16, ( void ** ) (ptrs_as + 2*ne23), CUDA_R_16F, ne01,
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&beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
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ne23,
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ne23,
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CUBLAS_COMPUTE_16F,
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CUBLAS_COMPUTE_16F,
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CUBLAS_GEMM_DEFAULT_TENSOR_OP));
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CUBLAS_GEMM_DEFAULT_TENSOR_OP));
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// free device memory for pointers
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if (ptrs_s != 0) {
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if (ptrs_src_s != 0) {
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ggml_cuda_pool_free_async(ptrs_as, ptrs_s, id, main_stream);
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ggml_cuda_pool_free_async(ptrs_src, ptrs_src_s, id, main_stream);
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}
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if (ptrs_dst_s != 0) {
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ggml_cuda_pool_free_async(ptrs_dst, ptrs_dst_s, id, main_stream);
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}
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}
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}
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}
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#endif
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#endif
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