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Add the keyword "template" to member template specialization appears after . or -> in a post-fix expression which is a requirement in C++ standard #1246

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Add the keyword "template" to member template specialization appears after . or -> in a post-fix expression which is a requirement in C++ standard #1246

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2 changes: 1 addition & 1 deletion include/flashinfer/attention/hopper/prefill_sm90.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -194,7 +194,7 @@ __global__ void __launch_bounds__(Ktraits::NUM_WARPS* cutlass::NumThreadsPerWarp
shared_storage, scheduler, scheduler_params, work_tile_info, block_coord, work_idx,
num_kv_tiles_outside_items_window, num_kv_tiles_prefix);
} else {
collective_mainloop.load<LEFT_SLIDING_WINDOW>(
collective_mainloop.template load<LEFT_SLIDING_WINDOW>(
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medium

The template keyword is added here to specify that load is a template method. Consider adding the template keyword to other template method calls within this file for consistency and to avoid potential compilation issues with strict compilers.

mainloop_params, pipeline_k, pipeline_v, smem_pipe_write_k, smem_pipe_write_v,
shared_storage, scheduler, scheduler_params, work_tile_info, block_coord, work_idx);
}
Expand Down
14 changes: 7 additions & 7 deletions include/flashinfer/attention/prefill.cuh
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Expand Up @@ -295,7 +295,7 @@ __device__ __forceinline__ void produce_kv(smem_t<KTraits::SWIZZLE_MODE_KV> smem
for (uint32_t i = 0; i < NUM_MMA_KV * 4 / NUM_WARPS_Q; ++i) {
#pragma unroll
for (uint32_t j = 0; j < NUM_MMA_D / (8 / sizeof(DTypeKV)); ++j) {
smem.load_128b_async<fill_mode>(*smem_offset, *gptr, kv_idx < kv_len);
smem.template load_128b_async<fill_mode>(*smem_offset, *gptr, kv_idx < kv_len);
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medium

The template keyword is correctly added here. Consider adding the template keyword to the call to advance_offset_by_column on line 299 for consistency, as it's also a member template function call on a dependent name.

*smem_offset = smem.template advance_offset_by_column<8>(*smem_offset, j);
*gptr += 8 * upcast_size<DTypeKV>();
}
Expand Down Expand Up @@ -434,7 +434,7 @@ __device__ __forceinline__ void load_q_global_smem(
const uint32_t lane_idx = tid.x, warp_idx_x = get_warp_idx_q<KTraits>(tid.y);

if (get_warp_idx_kv<KTraits>(tid.z) == 0) {
uint32_t q_smem_offset_w = q_smem->get_permuted_offset<UPCAST_STRIDE_Q>(
uint32_t q_smem_offset_w = q_smem->template get_permuted_offset<UPCAST_STRIDE_Q>(
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medium

The template keyword is correctly added here. Consider adding the template keyword to the call to advance_offset_by_column on line 454 for consistency, as it's also a member template function call on a dependent name.

warp_idx_x * KTraits::NUM_MMA_Q * 16 + lane_idx / 8, lane_idx % 8);

#pragma unroll
Expand All @@ -449,7 +449,7 @@ __device__ __forceinline__ void load_q_global_smem(
#pragma unroll
for (uint32_t mma_do = 0; mma_do < KTraits::NUM_MMA_D_QK / 4; ++mma_do) {
// load q fragment from gmem to smem
q_smem->load_128b_async<SharedMemFillMode::kNoFill>(q_smem_offset_w, q_ptr,
q_smem->template load_128b_async<SharedMemFillMode::kNoFill>(q_smem_offset_w, q_ptr,
q_idx < qo_upper_bound);
q_smem_offset_w = q_smem->template advance_offset_by_column<8>(q_smem_offset_w, mma_do);
q_ptr += 8 * upcast_size<DTypeQ>();
Expand Down Expand Up @@ -1251,12 +1251,12 @@ __device__ __forceinline__ void write_o_reg_gmem(
vec_cast<DTypeO, float>::cast<8>((DTypeO*)o_frag_f16, o_frag[mma_q][mma_d]);

#ifdef FLASHINFER_STMATRIX_M8N8X4_ENABLED
uint32_t o_smem_offset_w = o_smem->get_permuted_offset<UPCAST_STRIDE_O>(
uint32_t o_smem_offset_w = o_smem->template get_permuted_offset<UPCAST_STRIDE_O>(
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medium

The template keyword is correctly added here. Consider adding the template keyword to the call to get_permuted_offset on line 1259 and 1271 for consistency, as it's also a member template function call on a dependent name.

(warp_idx_x * KTraits::NUM_MMA_Q + mma_q) * 16 + lane_idx % 16,
mma_d * 2 + lane_idx / 16);
o_smem->stmatrix_m8n8x4(o_smem_offset_w, o_frag_f16);
#else
uint32_t o_smem_offset_w = o_smem->get_permuted_offset<UPCAST_STRIDE_O>(
uint32_t o_smem_offset_w = o_smem->template get_permuted_offset<UPCAST_STRIDE_O>(
(warp_idx_x * KTraits::NUM_MMA_Q + mma_q) * 16 + lane_idx / 4, mma_d * 2);
((uint32_t*)(o_smem->base + o_smem_offset_w))[lane_idx % 4] = o_frag_f16[0];
((uint32_t*)(o_smem->base + o_smem_offset_w + 8 * UPCAST_STRIDE_O))[lane_idx % 4] =
Expand All @@ -1268,7 +1268,7 @@ __device__ __forceinline__ void write_o_reg_gmem(
}
}

uint32_t o_smem_offset_w = o_smem->get_permuted_offset<UPCAST_STRIDE_O>(
uint32_t o_smem_offset_w = o_smem->template get_permuted_offset<UPCAST_STRIDE_O>(
warp_idx_x * KTraits::NUM_MMA_Q * 16 + lane_idx / 8, lane_idx % 8);

#pragma unroll
Expand Down Expand Up @@ -1412,7 +1412,7 @@ __device__ __forceinline__ void SinglePrefillWithKVCacheDevice(
? o + chunk_idx * o_stride_n + (kv_head_idx * group_size) * o_stride_h
: o + (kv_head_idx * group_size) * o_stride_h;

uint32_t q_smem_offset_r = qo_smem.get_permuted_offset<UPCAST_STRIDE_Q>(
uint32_t q_smem_offset_r = qo_smem.template get_permuted_offset<UPCAST_STRIDE_Q>(
get_warp_idx_q<KTraits>(tid.y) * NUM_MMA_Q * 16 + lane_idx % 16, lane_idx / 16);
load_q_global_smem<KTraits>(qo_packed_idx_base, qo_len, q_ptr_base, q_stride_n, q_stride_h,
group_size, &qo_smem, tid);
Expand Down
22 changes: 11 additions & 11 deletions include/flashinfer/sampling.cuh
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Expand Up @@ -292,7 +292,7 @@ __device__ __forceinline__ float GetMaxValue(float* in_data, uint32_t row_idx, u
}
max_val = max(
max_val, BlockReduce<float, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage.block_prim.reduce)
.Reduce<VEC_SIZE>(in_data_, cub::Max()));
.template Reduce<VEC_SIZE>(in_data_, cub::Max()));
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medium

The template keyword is added here to specify that Reduce is a template method. Consider adding the template keyword to other template method calls within this file for consistency and to avoid potential compilation issues with strict compilers.

__syncthreads();
}
if (tx == 0) {
Expand Down Expand Up @@ -599,7 +599,7 @@ __device__ __forceinline__ void DeviceSamplingFromProb(
}
float aggregate_local =
BlockReduce<float, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage->block_prim.reduce)
.Sum<VEC_SIZE>(prob_greater_than_threshold);
.template Sum<VEC_SIZE>(prob_greater_than_threshold);
if (tx == 0) {
temp_storage->block_aggregate.value = aggregate_local;
}
Expand All @@ -612,7 +612,7 @@ __device__ __forceinline__ void DeviceSamplingFromProb(
prob_greater_than_threshold, inclusive_cdf, temp_storage);
} else {
BlockScan<float, BLOCK_THREADS, SCAN_ALGORITHM>(temp_storage->block_prim.scan)
.InclusiveSum<VEC_SIZE>(prob_greater_than_threshold, inclusive_cdf);
.template InclusiveSum<VEC_SIZE>(prob_greater_than_threshold, inclusive_cdf);

__syncthreads();
}
Expand All @@ -628,7 +628,7 @@ __device__ __forceinline__ void DeviceSamplingFromProb(
.SubtractLeft<VEC_SIZE>(greater_than_u, greater_than_u_diff, BoolDiffOp());
#else
BlockAdjacentDifference<bool, BLOCK_THREADS>(temp_storage->block_prim.adj_diff)
.FlagHeads<VEC_SIZE>(greater_than_u_diff, greater_than_u, BoolDiffOp(), 0);
.template FlagHeads<VEC_SIZE>(greater_than_u_diff, greater_than_u, BoolDiffOp(), 0);
#endif
__syncthreads();

Expand Down Expand Up @@ -764,7 +764,7 @@ __global__ void SamplingFromLogitsKernel(DType* logits, IdType* output, IdType*

max_data +=
BlockReduce<DataAndIndex<DType, IdType>, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage)
.Sum<VEC_SIZE>(cur_data);
.template Sum<VEC_SIZE>(cur_data);
}
if (tx == 0) {
output[bx] = max_data.index;
Expand Down Expand Up @@ -1004,15 +1004,15 @@ __global__ void TopPSamplingFromProbKernel(DType* probs, IdType* output, IdType*
}

aggregate_gt_pivot_0 += BlockReduce<float, BLOCK_THREADS>(temp_storage.block_prim.reduce)
.Sum<VEC_SIZE>(probs_gt_pivot_0);
.template Sum<VEC_SIZE>(probs_gt_pivot_0);
if (tx == 0) {
temp_storage.block_aggregate.value = aggregate_gt_pivot_0;
}
__syncthreads();
aggregate_gt_pivot_0 = temp_storage.block_aggregate.value;

aggregate_gt_pivot_1 += BlockReduce<float, BLOCK_THREADS>(temp_storage.block_prim.reduce)
.Sum<VEC_SIZE>(probs_gt_pivot_1);
.template Sum<VEC_SIZE>(probs_gt_pivot_1);
if (tx == 0) {
temp_storage.block_aggregate.value = aggregate_gt_pivot_1;
}
Expand Down Expand Up @@ -1612,12 +1612,12 @@ __global__ void TopPRenormProbKernel(DType* probs, DType* renormed_prob, float*

aggregate_gt_pivot_0 +=
BlockReduce<float, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage.block_prim.reduce)
.Sum<VEC_SIZE>(probs_gt_pivot_0);
.template Sum<VEC_SIZE>(probs_gt_pivot_0);
__syncthreads();

aggregate_gt_pivot_1 +=
BlockReduce<float, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage.block_prim.reduce)
.Sum<VEC_SIZE>(probs_gt_pivot_1);
.template Sum<VEC_SIZE>(probs_gt_pivot_1);
__syncthreads();
}
min_gt_low = BlockReduce<float, BLOCK_THREADS, REDUCE_ALGORITHM>(temp_storage.block_prim.reduce)
Expand Down Expand Up @@ -1853,12 +1853,12 @@ __global__ void TopKRenormProbKernel(DType* probs, DType* renormed_prob, IdType*

aggregate_gt_pivot_0 += BlockReduce<ValueCount<float>, BLOCK_THREADS, REDUCE_ALGORITHM>(
temp_storage.block_prim.reduce_value_count)
.Sum<VEC_SIZE>(probs_gt_pivot_0_pair);
.template Sum<VEC_SIZE>(probs_gt_pivot_0_pair);
__syncthreads();

aggregate_gt_pivot_1 += BlockReduce<ValueCount<float>, BLOCK_THREADS, REDUCE_ALGORITHM>(
temp_storage.block_prim.reduce_value_count)
.Sum<VEC_SIZE>(probs_gt_pivot_1_pair);
.template Sum<VEC_SIZE>(probs_gt_pivot_1_pair);
__syncthreads();
}
min_gt_low =
Expand Down
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