diff options
| -rw-r--r-- | ggml-cuda.cu | 123 | ||||
| -rw-r--r-- | ggml-cuda.h | 2 | ||||
| -rw-r--r-- | ggml.c | 92 | ||||
| -rw-r--r-- | llama-util.h | 6 | ||||
| -rw-r--r-- | llama.cpp | 197 |
5 files changed, 304 insertions, 116 deletions
diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 688bcf7..35d2e45 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -83,9 +83,19 @@ typedef struct { } block_q8_0; static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); +#define CUDA_MUL_BLOCK_SIZE 256 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256 #define CUDA_DMMV_BLOCK_SIZE 32 // dmmv = dequantize_mul_mat_vec +static __global__ void mul_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 >= kx) { + return; + } + dst[i] = x[i] * y[i%ky]; +} + static __device__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){ const block_q4_0 * x = (const block_q4_0 *) vx; @@ -228,6 +238,11 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, } } +static void mul_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_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE; + mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky); +} + static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k); @@ -467,6 +482,67 @@ static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor } } +static void ggml_cuda_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src1->backend == GGML_BACKEND_CUDA); + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[2]; + const int64_t ne0 = ne00 * ne01 * ne02 * ne03; + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; + const int nb2 = dst->nb[2]; + const int nb3 = dst->nb[3]; + size_t x_size, d_size; + + float * d_X = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &x_size); // src0 + float * d_Y = (float *) src1->data; // src1 is already on device, broadcasted. + float * d_D = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &d_size); // dst + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + const int i0 = i03*ne02 + i02; + float * c_X2 = d_X + i0*ne01*ne00; + float * c_D2 = d_D + i0*ne01*ne00; + + cudaStream_t cudaStream = g_cudaStreams[i0 % GGML_CUDA_MAX_STREAMS]; + cudaStream_t cudaStream2 = g_cudaStreams2[i0 % GGML_CUDA_MAX_STREAMS]; + cudaEvent_t cudaEvent = g_cudaEvents[i0 % GGML_CUDA_MAX_EVENTS]; + + // copy src0 to device + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X2, src0, i03, i02, cudaStream2)); + CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2)); + + // wait for data + CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0)); + + for (int64_t i01 = 0; i01 < ne01; i01++) { + const int64_t i13 = i03%ne13; + const int64_t i12 = i02%ne12; + const int64_t i11 = i01%ne11; + const int i1 = i13*ne12*ne11 + i12*ne11 + i11; + + float * c_X1 = c_X2 + i01*ne00; + float * c_Y = d_Y + i1*ne10; + float * c_D1 = c_D2 + i01*ne00; + + // compute + mul_f32_cuda(c_X1, c_Y, c_D1, ne00, ne10, cudaStream); + CUDA_CHECK(cudaGetLastError()); + } + + // copy dst to host + float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); + CUDA_CHECK(cudaMemcpyAsync(d, c_D2, sizeof(float)*ne00*ne01, cudaMemcpyDeviceToHost, cudaStream)); + } + } + CUDA_CHECK(cudaDeviceSynchronize()); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_D, d_size); +} + static void ggml_cuda_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; @@ -724,6 +800,11 @@ static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor ggml_cuda_pool_free(d_Q, q_size); } +void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + ggml_cuda_mul_f32(src0, src1, dst); +} + bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { const int64_t ne10 = src1->ne[0]; @@ -797,14 +878,48 @@ void ggml_cuda_transform_tensor(ggml_tensor * tensor) { const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type); size_t q_size; - char * d_Q = (char *) ggml_cuda_pool_malloc(q_sz, &q_size); + char * dst = (char *) ggml_cuda_pool_malloc(q_sz, &q_size); cudaStream_t cudaStream2 = g_cudaStreams2[0]; // copy tensor to device - CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, tensor, 0, 0, cudaStream2)); - CUDA_CHECK(cudaDeviceSynchronize()); + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + int i = i3*ne2 + i2; + CUDA_CHECK(ggml_cuda_h2d_tensor_2d(dst + i*ne0*ne1, tensor, i3, i2, cudaStream2)); + } + } - tensor->data = d_Q; + tensor->data = dst; tensor->backend = GGML_BACKEND_CUDA; } + +void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) { + FILE * fp = fopen(fname, "rb"); + + const size_t size = ggml_nbytes(tensor); + + void * buf; + CUDA_CHECK(cudaMalloc(&buf, size)); + void * buf_host = malloc(size); + +#ifdef _WIN32 + int ret = _fseeki64(fp, (__int64) offset, SEEK_SET); +#else + int ret = fseek(fp, (long) offset, SEEK_SET); +#endif + GGML_ASSERT(ret == 0); // same + + size_t ret2 = fread(buf_host, size, 1, fp); + if (ret2 != 1) { + fprintf(stderr, "unexpectedly reached end of file"); + exit(1); + } + + cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice); + cudaDeviceSynchronize(); + + tensor->data = buf; + free(buf_host); + fclose(fp); +} diff --git a/ggml-cuda.h b/ggml-cuda.h index 4e2c242..6a04dde 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -6,6 +6,7 @@ extern "C" { void ggml_init_cublas(void); +void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); @@ -15,6 +16,7 @@ void * ggml_cuda_host_malloc(size_t size); void ggml_cuda_host_free(void * ptr); void ggml_cuda_transform_tensor(struct ggml_tensor * tensor); +void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset); #ifdef __cplusplus } @@ -3776,6 +3776,12 @@ static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct g (t1->ne[3]%t0->ne[3] == 0); } +static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1); +} + static inline int ggml_up32(int n) { return (n + 31) & ~31; } @@ -4658,11 +4664,15 @@ struct ggml_tensor * ggml_mul_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 (!inplace && (a->grad || b->grad)) { + // TODO: support backward pass for broadcasting + GGML_ASSERT(ggml_are_same_shape(a, b)); is_node = true; } @@ -7960,7 +7970,7 @@ static void ggml_compute_forward_mul_f32( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - 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; @@ -7968,10 +7978,25 @@ static void ggml_compute_forward_mul_f32( const int ith = params->ith; const int nth = params->nth; - const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; +#ifdef GGML_USE_CUBLAS + if (src1->backend == GGML_BACKEND_CUDA) { + if (ith == 0) { + ggml_cuda_mul(src0, src1, dst); + } + return; + } +#endif + + const int64_t nr = ggml_nrows(src0); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; const size_t nb00 = src0->nb[0]; const size_t nb01 = src0->nb[1]; @@ -7990,44 +8015,51 @@ static void ggml_compute_forward_mul_f32( GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(ne00 == ne10); if (nb10 == sizeof(float)) { - for (int ir = ith; ir < nr; ir += nth) { - // 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); + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + 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 UNUSED(ggml_vec_mul_f32); - vDSP_vmul( - (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_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00); #else - ggml_vec_mul_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_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr); #endif // } // } } } else { // src1 is not contiguous - for (int ir = ith; ir < nr; ir += nth) { - // 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); - - 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); + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + // 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); + + for (int64_t i0 = 0; i0 < ne00; i0++) { + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10); dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); } diff --git a/llama-util.h b/llama-util.h index a79c5da..3cac9f6 100644 --- a/llama-util.h +++ b/llama-util.h @@ -172,7 +172,7 @@ struct llama_mmap { #ifdef _POSIX_MAPPED_FILES static constexpr bool SUPPORTED = true; - llama_mmap(struct llama_file * file, bool prefetch = true) { + llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) { size = file->size; int fd = fileno(file->fp); int flags = MAP_SHARED; @@ -184,9 +184,9 @@ struct llama_mmap { throw std::runtime_error(format("mmap failed: %s", strerror(errno))); } - if (prefetch) { + if (prefetch > 0) { // Advise the kernel to preload the mapped memory - if (madvise(addr, file->size, MADV_WILLNEED)) { + if (madvise(addr, std::min(file->size, prefetch), MADV_WILLNEED)) { fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n", strerror(errno)); } @@ -1,6 +1,7 @@ // Defines fileno on msys: #ifndef _GNU_SOURCE #define _GNU_SOURCE +#include <cstddef> #include <cstdint> #include <cstdio> #endif @@ -645,7 +646,7 @@ struct llama_model_loader { } } - struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne) { + struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) { auto it = tensors_map.name_to_idx.find(name); if (it == tensors_map.name_to_idx.end()) { throw format("llama.cpp: tensor '%s' is missing from model", name.c_str()); @@ -656,10 +657,10 @@ struct llama_model_loader { name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()); } - return get_tensor_for(lt); + return get_tensor_for(lt, backend); } - struct ggml_tensor * get_tensor_for(llama_load_tensor & lt) { + struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, ggml_backend backend) { struct ggml_tensor * tensor; if (lt.ne.size() == 2) { tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1)); @@ -669,6 +670,7 @@ struct llama_model_loader { } ggml_set_name(tensor, lt.name.c_str()); LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor + tensor->backend = backend; lt.ggml_tensor = tensor; num_ggml_tensors_created++; return tensor; @@ -682,12 +684,16 @@ struct llama_model_loader { void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) { size_t data_size = 0; + size_t prefetch_size = 0; for (const llama_load_tensor & lt : tensors_map.tensors) { data_size += lt.size; + if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) { + prefetch_size += lt.size; + } } if (use_mmap) { - mapping.reset(new llama_mmap(&file_loaders.at(0)->file)); + mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size)); if (!lmlock) { // Don't call the callback since the actual loading will be lazy // and we can't measure it. @@ -700,6 +706,9 @@ struct llama_model_loader { size_t done_size = 0; for (llama_load_tensor & lt : tensors_map.tensors) { + if (lt.ggml_tensor->backend != GGML_BACKEND_CPU) { + continue; + } if (progress_callback) { progress_callback((float) done_size / data_size, progress_callback_user_data); } @@ -712,9 +721,6 @@ struct llama_model_loader { lmlock->grow_to(done_size); } } - if (progress_callback) { - progress_callback(1.0f, progress_callback_user_data); - } } void load_data_for(llama_load_tensor & lt) { @@ -969,27 +975,7 @@ static void llama_model_load_internal( size_t ctx_size; size_t mmapped_size; ml->calc_sizes(&ctx_size, &mmapped_size); - fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/1024.0/1024.0); - - // print memory requirements - { - const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1; - - // this is the total memory required to run the inference - const size_t mem_required = - ctx_size + - mmapped_size + - MEM_REQ_SCRATCH0().at(model.type) + - MEM_REQ_SCRATCH1().at(model.type) + - MEM_REQ_EVAL().at(model.type); - - // this is the memory required by one llama_state - const size_t mem_required_state = - scale*MEM_REQ_KV_SELF().at(model.type); - - fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__, - mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0); - } + fprintf(stderr, "%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0); // create the ggml context { @@ -1011,7 +997,14 @@ static void llama_model_load_internal( } } +#ifdef GGML_USE_CUBLAS +#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CUDA +#else +#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU +#endif + // prepare memory for the weights + size_t vram_total = 0; { const uint32_t n_embd = hparams.n_embd; const uint32_t n_layer = hparams.n_layer; @@ -1019,70 +1012,122 @@ static void llama_model_load_internal( ml->ggml_ctx = ctx; - model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}); - model.norm = ml->get_tensor("norm.weight", {n_embd}); - model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}); + model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU); + model.norm = ml->get_tensor("norm.weight", {n_embd}, GGML_BACKEND_CPU); + + // "output" tensor + { + ggml_backend backend_output; + if (n_gpu_layers > int(n_layer)) { // NOLINT + backend_output = LLAMA_BACKEND_OFFLOAD; + } else { + backend_output = GGML_BACKEND_CPU; + } + + model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output); + } + + const int i_gpu_start = n_layer - n_gpu_layers; model.layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { + const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; + auto & layer = model.layers[i]; std::string layers_i = "layers." + std::to_string(i); - layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}); + layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend); - layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}); + layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend); + layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend); + layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend); + layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend); - layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}); + layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend); - layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}); - layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}); - layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}); + layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend); + layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend); + layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend); + + if (backend == GGML_BACKEND_CUDA) { + vram_total += + ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) + + ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3); + } } } ml->done_getting_tensors(); - // populate `tensors_by_name` - for (llama_load_tensor & lt : ml->tensors_map.tensors) { - model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor); - } + // print memory requirements + { + const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1; - ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL); + // this is the total memory required to run the inference + const size_t mem_required = + ctx_size + + mmapped_size - vram_total + // weights in VRAM not in memory + MEM_REQ_SCRATCH0().at(model.type) + + MEM_REQ_SCRATCH1().at(model.type) + + MEM_REQ_EVAL().at(model.type); + + // this is the memory required by one llama_state + const size_t mem_required_state = + scale*MEM_REQ_KV_SELF().at(model.type); + + fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__, + mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0); - model.mapping = std::move(ml->mapping); #ifdef GGML_USE_CUBLAS - { const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu); + if (n_gpu_layers > (int) hparams.n_layer) { + fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__); + } + fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); +#else + (void) n_gpu_layers; +#endif + } - size_t vram_total = 0; + // populate `tensors_by_name` + for (llama_load_tensor & lt : ml->tensors_map.tensors) { + model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor); + } - for (int i = 0; i < n_gpu; ++i) { - const auto & layer = model.layers[i]; + ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL); - ggml_cuda_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq); - ggml_cuda_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk); - ggml_cuda_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv); - ggml_cuda_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo); - ggml_cuda_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1); - ggml_cuda_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2); - ggml_cuda_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3); +#ifdef GGML_USE_CUBLAS + { + size_t done_size = 0; + size_t data_size = 0; + for (llama_load_tensor & lt : ml->tensors_map.tensors) { + data_size += lt.size; + if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) { + done_size += lt.size; + } } - if (n_gpu_layers > (int) hparams.n_layer) { - fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__); - ggml_cuda_transform_tensor(model.output); vram_total += ggml_nbytes(model.output); + for (llama_load_tensor & lt : ml->tensors_map.tensors) { + if (lt.ggml_tensor->backend != GGML_BACKEND_CUDA) { + continue; + } + if (progress_callback) { + progress_callback((float) done_size / data_size, progress_callback_user_data); + } + ggml_cuda_load_data(fname.c_str(), lt.ggml_tensor, lt.shards.at(0).file_off); + done_size += lt.size; } + } +#endif // GGML_USE_CUBLAS - fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); + if (progress_callback) { + progress_callback(1.0f, progress_callback_user_data); } -#else - (void) n_gpu_layers; -#endif + + model.mapping = std::move(ml->mapping); // loading time will be recalculate after the first eval, so // we take page faults deferred by mmap() into consideration @@ -1181,10 +1226,8 @@ static bool llama_eval_internal( { cur = ggml_rms_norm(ctx0, inpL); - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].attention_norm, cur), - cur); + // cur = cur*attention_norm(broadcasted) + cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm); } // self-attention @@ -1291,10 +1334,8 @@ static bool llama_eval_internal( { cur = ggml_rms_norm(ctx0, inpFF); - // cur = ffn_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].ffn_norm, cur), - cur); + // cur = cur*ffn_norm(broadcasted) + cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm); } struct ggml_tensor * tmp = ggml_mul_mat(ctx0, @@ -1331,10 +1372,8 @@ static bool llama_eval_internal( inpL = ggml_rms_norm(ctx0, inpL); - // inpL = norm*inpL - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model.norm, inpL), - inpL); + // inpL = inpL*norm(broadcasted) + inpL = ggml_mul(ctx0, inpL, model.norm); embeddings = inpL; } @@ -2158,7 +2197,7 @@ struct llama_context * llama_init_from_file( unsigned * cur_percentage_p = (unsigned *) ctx; unsigned percentage = (unsigned) (100 * progress); while (percentage > *cur_percentage_p) { - ++*cur_percentage_p; + *cur_percentage_p = percentage; fprintf(stderr, "."); fflush(stderr); if (percentage >= 100) { @@ -2315,7 +2354,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * // maybe this should in llama_model_loader if (model_loader->use_mmap) { - model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ false)); + model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0)); } } @@ -2408,7 +2447,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * } size_t idx = model_loader->tensors_map.name_to_idx[base_name]; llama_load_tensor & lt = model_loader->tensors_map.tensors[idx]; - base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }); + base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU); lt.data = (uint8_t *) lt.ggml_tensor->data; model_loader->load_data_for(lt); lt.ggml_tensor->data = lt.data; |