mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-23 21:17:54 +01:00
ggml : fix some mul mat cases + add tests for src1 F16 (ggml/669)
* fixed mul-mat error for old GPUs * style fixes * add mul mat src1 f16 test cases, fix more cases ggml-ci --------- Co-authored-by: bssrdf <bssrdf@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
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ca38b8d334
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afc8c19291
@ -614,10 +614,14 @@ static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
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}
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static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
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return true;
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switch (op->op) {
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case GGML_OP_MUL_MAT:
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return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
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default:
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return true;
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}
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GGML_UNUSED(backend);
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GGML_UNUSED(op);
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}
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static struct ggml_backend_i cpu_backend_i = {
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89
ggml-cuda.cu
89
ggml-cuda.cu
@ -7485,6 +7485,8 @@ static void ggml_cuda_op_dequantize_mul_mat_vec(
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const int64_t ne00 = src0->ne[0];
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const int64_t row_diff = row_high - row_low;
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
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#ifdef GGML_CUDA_F16
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cuda_pool_alloc<half> src1_dfloat_a;
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@ -7577,6 +7579,7 @@ static void ggml_cuda_op_mul_mat_cublas(
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const int compute_capability = g_device_caps[id].cc;
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if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
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//printf("this branch\n");
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// convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
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cuda_pool_alloc<half> src0_as_f16;
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if (src0->type != GGML_TYPE_F16) {
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@ -7614,9 +7617,9 @@ static void ggml_cuda_op_mul_mat_cublas(
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const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
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to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
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}
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else {
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} else {
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cuda_pool_alloc<float> src0_ddq_as_f32;
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cuda_pool_alloc<float> src1_ddq_as_f32;
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if (src0->type != GGML_TYPE_F32) {
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const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
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@ -7624,7 +7627,15 @@ static void ggml_cuda_op_mul_mat_cublas(
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src0_ddq_as_f32.alloc(row_diff*ne00);
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to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream);
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}
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if (src1->type != GGML_TYPE_F32) {
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const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src1->type);
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GGML_ASSERT(to_fp32_cuda != nullptr);
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src1_ddq_as_f32.alloc(src1_ncols*ne10);
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to_fp32_cuda(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream);
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}
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const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
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const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
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const float alpha = 1.0f;
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const float beta = 0.0f;
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@ -7633,9 +7644,9 @@ static void ggml_cuda_op_mul_mat_cublas(
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CUBLAS_CHECK(
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cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
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row_diff, src1_ncols, ne10,
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&alpha, src0_ddf_i, ne00,
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src1_ddf_i, ne10,
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&beta, dst_dd_i, ldc));
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&alpha, src0_ddf_i, ne00,
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src1_ddf1_i, ne10,
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&beta, dst_dd_i, ldc));
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}
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(void) dst;
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@ -8035,6 +8046,7 @@ static void ggml_cuda_op_mul_mat(
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GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
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GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
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GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
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GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
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@ -8481,9 +8493,9 @@ static __global__ void k_compute_batched_ptrs(
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int64_t i03 = i13 / r3;
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int64_t i02 = i12 / r2;
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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_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
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ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3;
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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_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13;
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ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3;
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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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@ -8492,28 +8504,10 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
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GGML_ASSERT(src0->type == GGML_TYPE_F16);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00);
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const int64_t ne01 = src0->ne[1];
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const int64_t ne02 = src0->ne[2];
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const int64_t ne03 = src0->ne[3];
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GGML_TENSOR_BINARY_OP_LOCALS
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const int64_t nb01 = src0->nb[1];
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const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02);
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const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03);
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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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const int64_t ne12 = src1->ne[2];
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const int64_t ne13 = src1->ne[3];
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const int64_t nb11 = src1->nb[1];
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const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
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const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
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const int64_t ne1 = ggml_nelements(src1);
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const int64_t ne = ggml_nelements(dst);
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const int64_t ne_dst = ggml_nelements(dst);
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ggml_cuda_set_device(g_main_device);
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cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
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@ -8522,7 +8516,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
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void * src0_ddq = src0_extra->data_device[g_main_device];
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half * src0_as_f16 = (half *) src0_ddq;
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half * src0_f16 = (half *) src0_ddq;
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ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
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float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
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@ -8531,11 +8525,15 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
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// convert src1 to fp16
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const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
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GGML_ASSERT(to_fp16_cuda != nullptr);
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cuda_pool_alloc<half> src1_as_f16(ne1);
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to_fp16_cuda(src1_ddf, src1_as_f16.get(), ne1, main_stream);
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cuda_pool_alloc<half> src1_f16_alloc;
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if (src1->type != GGML_TYPE_F16) {
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const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
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const int64_t ne_src1 = ggml_nelements(src1);
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src1_f16_alloc.alloc(ne_src1);
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GGML_ASSERT(to_fp16_cuda != nullptr);
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to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream);
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}
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half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get();
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cuda_pool_alloc<half> dst_f16;
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char * dst_t;
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@ -8557,7 +8555,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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const void * beta = &beta_f16;
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if (dst->op_params[0] == GGML_PREC_DEFAULT) {
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dst_t = (char *) dst_f16.alloc(ne);
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dst_t = (char *) dst_f16.alloc(ne_dst);
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nbd2 /= sizeof(float) / sizeof(half);
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nbd3 /= sizeof(float) / sizeof(half);
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@ -8604,9 +8602,9 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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CUBLAS_CHECK(
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cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
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ne01, ne11, ne10,
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alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA
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(const char *) src1_as_f16.get(), CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
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beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC
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alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA
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(const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB
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beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC
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ne12*ne13,
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cu_compute_type,
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CUBLAS_GEMM_DEFAULT_TENSOR_OP));
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@ -8619,12 +8617,13 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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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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src0_as_f16, src1_as_f16.get(), dst_t,
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src0_f16, src1_f16, dst_t,
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ptrs_src.get(), ptrs_dst.get(),
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ne12, ne13,
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ne23,
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nb02, nb03,
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nb12, nb13,
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src1->type == GGML_TYPE_F16 ? nb12 : nb12/2,
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src1->type == GGML_TYPE_F16 ? nb13 : nb13/2,
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nbd2, nbd3,
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r2, r3);
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CUDA_CHECK(cudaGetLastError());
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@ -8632,8 +8631,8 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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CUBLAS_CHECK(
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cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
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ne01, ne11, ne10,
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alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
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(const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
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alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00,
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(const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10,
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beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01,
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ne23,
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cu_compute_type,
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@ -8643,7 +8642,7 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
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if (dst->op_params[0] == GGML_PREC_DEFAULT) {
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const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
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to_fp32_cuda(dst_f16.get(), dst_ddf, ne, main_stream);
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to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream);
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}
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}
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@ -8682,13 +8681,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
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} else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
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// KQV single-batch
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ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
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} else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
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} else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
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// KQ + KQV multi-batch
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ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
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} else if (src0->type == GGML_TYPE_F32) {
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ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
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} else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
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if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
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if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->type == GGML_TYPE_F32) {
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#ifdef GGML_CUDA_FORCE_DMMV
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const bool use_mul_mat_vec_q = false;
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#else
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2
ggml.c
2
ggml.c
@ -9687,7 +9687,7 @@ static void ggml_compute_forward_mul_mat(
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const size_t row_size = ggml_row_size(vec_dot_type, ne10);
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assert(params->wsize >= ne11*ne12*ne13*row_size);
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assert(src1->type == GGML_TYPE_F32);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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for (int64_t i13 = 0; i13 < ne13; ++i13) {
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for (int64_t i12 = 0; i12 < ne12; ++i12) {
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@ -350,13 +350,18 @@ struct test_case {
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fflush(stdout);
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// check if backends support op
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bool supported = true;
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for (ggml_backend_t backend : {backend1, backend2}) {
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if (!ggml_backend_supports_op(backend, out)) {
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printf("not supported\n");
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ggml_free(ctx);
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return true;
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printf("not supported [%s] ", ggml_backend_name(backend));
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supported = false;
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}
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}
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if (!supported) {
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printf("\n");
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ggml_free(ctx);
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return true;
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}
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// post-graph sentinel
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add_sentinel(ctx);
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@ -1505,8 +1510,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
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}
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for (ggml_type type_a : all_types) {
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for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
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// FIXME: CPU crashes on f16xf16
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for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
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test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
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test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1}));
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test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {2, 1}));
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