Review: make check more explicit, add back comments, and fix formatting

Author: am17an

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -1772,7 +1772,7 @@ static __global__ void k_compute_batched_ptrs(
     ptrs_dst[0*ne23 + i12 + i13*ne12] = (      char *)         dst + i12*nbd2 + i13*nbd3;
 }
 
-// Type traits for CUDA types
+// Type traits for mapping ggml types to CUDA/cuBLAS types
 template<ggml_type T>
 struct batched_mul_mat_traits;
 
@@ -1826,6 +1826,9 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
     GGML_ASSERT(src0->type == src0_type);
     GGML_ASSERT(ggml_is_contiguous(dst));
 
+    // Byte offsets and tensor dimensions are currently used in an inconsistent way for dst.
+    // As long as dst is contiguous this does not matter though.
+
     GGML_TENSOR_BINARY_OP_LOCALS
 
     const int64_t ne_dst = ggml_nelements(dst);
@@ -1877,6 +1880,8 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
     cudaDataType_t cu_data_type_b = traits::data_type;
     const void * alpha = traits::get_alpha();
     const void * beta = traits::get_beta();
+    const float alpha_f32 = 1.0f;
+    const float beta_f32 = 0.0f;
 
     if (dst->op_params[0] == GGML_PREC_DEFAULT) {
         if constexpr (src0_type == GGML_TYPE_F32) {
@@ -1890,8 +1895,6 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
         dst_t = (char *) dst_ddf;
         cu_compute_type = CUBLAS_COMPUTE_32F;
         cu_data_type = CUDA_R_32F;
-        const float alpha_f32 = 1.0f;
-        const float beta_f32 = 0.0f;
         alpha = &alpha_f32;
         beta = &beta_f32;
     }
@@ -1900,8 +1903,6 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
     const int cc = ggml_cuda_info().devices[id].cc;
     if (GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
         cu_compute_type = CUBLAS_COMPUTE_32F;
-        const float alpha_f32 = 1.0f;
-        const float beta_f32 = 0.0f;
         alpha = &alpha_f32;
         beta = &beta_f32;
     }
@@ -2032,6 +2033,11 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
+    const int cc                     = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    bool can_use_batched_cublas_f16  = src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16);
+    bool can_use_batched_cublas_bf16 = src0->type == GGML_TYPE_BF16 && bf16_mma_hardware_available(cc);
+    bool can_use_batched_cublas_f32  = src0->type == GGML_TYPE_F32;
+
     if (!split && use_mul_mat_vec) {
         // the custom F16 vector kernel can be used over batched cuBLAS GEMM
         // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention)
@@ -2040,8 +2046,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
         ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_q) {
         ggml_cuda_mul_mat_q(ctx, src0, src1, nullptr, dst);
-    } else if (!split && (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16 || src0->type == GGML_TYPE_F32)
-        && (src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_BF16 || src1->type == GGML_TYPE_F32)
+    } else if (!split && (can_use_batched_cublas_f16 || can_use_batched_cublas_bf16 || can_use_batched_cublas_f32)
         && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
         // general KQ + KQV multi-batch without FlashAttention
         ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);

tests/test-backend-ops.cpp

@@ -4425,7 +4425,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         for (auto nr : {1,4}) {
             for (uint32_t m = 0; m < 2; ++m) {
                 for (uint32_t k = 0; k < 2; ++k) {
-                    for(ggml_type type: {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}){
+                    for (ggml_type type: {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
                         test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 1056 + m, 1, 128 + k,  {bs,  1}, {nr, 1}, {0, 2, 1, 3}));
                         test_cases.emplace_back(new test_mul_mat(type, GGML_TYPE_F32, 128 + m,  1, 1056 + k, {bs,  1}, {nr, 1}, {0, 1, 2, 3}, true));
                     }