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Optimize sparse logic with block-level Tensor Core utilization #110

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7a958a5
Initial plan
Copilot Aug 15, 2025
2309e82
Implement block-level sparse GEMM optimization
Copilot Aug 15, 2025
c11624d
Complete sparse GEMM optimization with documentation
Copilot Aug 15, 2025
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174 changes: 117 additions & 57 deletions csrc/src/utils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -180,48 +180,70 @@ __forceinline__ __device__ void sparse_gemm(
CUTE_STATIC_ASSERT_V(size<1>(tCsA) == size<1>(tCrA_copy_view)); // M
auto tCrB_copy_view = smem_thr_copy_B.retile_D(tCrB);
CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<1>(tCrB_copy_view)); // N
// Check if any element in the entire active mask is non-zero
// Use thread-local computation then sync across all threads in the CTA
bool local_any_active = false;

// Approach 2: Count and batch active KV blocks for uniform computation
// First, analyze sparsity pattern to identify which computation blocks need processing
constexpr int num_mma_blocks = decltype(size<0>(tCrM))::value;
bool mma_block_active[num_mma_blocks];
int active_block_count = 0;

#pragma unroll
for (int mma = 0; mma < size<0>(tCrM) && !local_any_active; ++mma) {
for (int mma = 0; mma < size<0>(tCrM); ++mma) {
bool local_has_active = false;
#pragma unroll
for (int m = 0; m < size<1>(tCrM) && !local_any_active; ++m) {
for (int m = 0; m < size<1>(tCrM) && !local_has_active; ++m) {
#pragma unroll
for (int n = 0; n < size<2>(tCrM) && !local_any_active; ++n) {
// Use direct comparison to avoid potential branching
local_any_active |= (tCrM(mma, m, n) > 0);
for (int n = 0; n < size<2>(tCrM) && !local_has_active; ++n) {
local_has_active |= (tCrM(mma, m, n) > 0);
}
}
// Synchronize to ensure consistent view across CTA
mma_block_active[mma] = __syncthreads_or(local_has_active);
if (mma_block_active[mma]) {
active_block_count++;
}
}
// Ensure all threads in the CTA have the same any_active value to avoid warp divergence
bool any_active = __syncthreads_or(local_any_active);
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, _0{}), tCrA_copy_view(_, _, _0{})); }
if (!B_in_regs) {
if (any_active) {
// If any MMA block is active, load normally like dense gemm
cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{}));
} else {
// If no MMA block is active, clear all registers
cute::clear(tCrB_copy_view);

// Early exit optimization: if no blocks are active, skip all computation
if (active_block_count == 0) {
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, _0{}), tCrA_copy_view(_, _, _0{})); }
if (!B_in_regs) { cute::clear(tCrB_copy_view); }
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, i + 1), tCrA_copy_view(_, _, i + 1)); }
if (!B_in_regs) { cute::clear(tCrB_copy_view(_, _, i + 1)); }
}
// Skip GEMM computation entirely - results will remain zero
}
return;
}
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, i + 1), tCrA_copy_view(_, _, i + 1)); }
if (!B_in_regs) {
if (any_active) {
// If any MMA block is active, load normally like dense gemm
cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1));
} else {
// If no MMA block is active, clear all registers
cute::clear(tCrB_copy_view(_, _, i + 1));
}

// Approach 1: Early branching - separate dense and sparse computation paths
if (active_block_count == num_mma_blocks) {
// Dense path: all blocks are active, use standard dense GEMM
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, _0{}), tCrA_copy_view(_, _, _0{})); }
if (!B_in_regs) { cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{})); }
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, i + 1), tCrA_copy_view(_, _, i + 1)); }
if (!B_in_regs) { cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1)); }
}
// Dense computation - all Tensor Cores fully utilized
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
}
// Only perform GEMM if there are any active elements
if (any_active) {
} else {
// Sparse path: mixed sparsity pattern, load data and compute with mask awareness
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, _0{}), tCrA_copy_view(_, _, _0{})); }
if (!B_in_regs) { cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{})); }
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
if (!A_in_regs) { cute::copy(smem_tiled_copy_A, tCsA(_, _, i + 1), tCrA_copy_view(_, _, i + 1)); }
if (!B_in_regs) { cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1)); }
}
// Mixed sparse computation - some Tensor Cores utilized, mask will handle fine-grained sparsity
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
}
}
Expand Down Expand Up @@ -268,42 +290,80 @@ __forceinline__ __device__ void sparse_gemm_rs(
// Retile B for thread-wise copy from shared memory to registers
auto tCrB_copy_view = smem_thr_copy_B.retile_D(tCrB);
CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<1>(tCrB_copy_view)); // N
// Check if any element in the entire active mask is non-zero
// Use thread-local computation then sync across all threads in the CTA
bool local_any_active = false;
// Block-level sparsity analysis: check each MMA block individually for better Tensor Core utilization
bool block_active[decltype(size<0>(tCrM))::value];
bool any_block_active = false;
#pragma unroll
for (int mma = 0; mma < size<0>(tCrM) && !local_any_active; ++mma) {
for (int mma = 0; mma < size<0>(tCrM); ++mma) {
bool local_mma_active = false;
#pragma unroll
for (int m = 0; m < size<1>(tCrM) && !local_any_active; ++m) {
for (int m = 0; m < size<1>(tCrM) && !local_mma_active; ++m) {
#pragma unroll
for (int n = 0; n < size<2>(tCrM) && !local_any_active; ++n) {
// Use direct comparison to avoid potential branching
local_any_active |= (tCrM(mma, m, n) > 0);
for (int n = 0; n < size<2>(tCrM) && !local_mma_active; ++n) {
local_mma_active |= (tCrM(mma, m, n) > 0);
}
}
// Synchronize activity status across all threads in the CTA for this MMA block
block_active[mma] = __syncthreads_or(local_mma_active);
any_block_active |= block_active[mma];
}
// Ensure all threads in the CTA have the same any_active value to avoid warp divergence
bool any_active = __syncthreads_or(local_any_active);
if (any_active) {
// If any MMA block is active, load normally like dense gemm
cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{}));
} else {
// If no MMA block is active, clear all registers
// Approach 2: Count and batch active KV blocks for uniform computation
// First, analyze sparsity pattern to identify which computation blocks need processing
constexpr int num_mma_blocks = decltype(size<0>(tCrM))::value;
bool mma_block_active[num_mma_blocks];
int active_block_count = 0;

#pragma unroll
for (int mma = 0; mma < size<0>(tCrM); ++mma) {
bool local_has_active = false;
#pragma unroll
for (int m = 0; m < size<1>(tCrM) && !local_has_active; ++m) {
#pragma unroll
for (int n = 0; n < size<2>(tCrM) && !local_has_active; ++n) {
local_has_active |= (tCrM(mma, m, n) > 0);
}
}
// Synchronize to ensure consistent view across CTA
mma_block_active[mma] = __syncthreads_or(local_has_active);
if (mma_block_active[mma]) {
active_block_count++;
}
}

// Early exit optimization: if no blocks are active, skip all computation
if (active_block_count == 0) {
cute::clear(tCrB_copy_view);
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
cute::clear(tCrB_copy_view(_, _, i + 1));
}
// Skip GEMM computation entirely - results will remain zero
}
return;
}
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
if (any_active) {
// If any MMA block is active, load normally like dense gemm

// Approach 1: Early branching - separate dense and sparse computation paths
if (active_block_count == num_mma_blocks) {
// Dense path: all blocks are active, use standard dense GEMM
cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{}));
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1));
} else {
// If no MMA block is active, clear all registers
cute::clear(tCrB_copy_view(_, _, i + 1));
}
// Dense computation - all Tensor Cores fully utilized
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
}
// Only perform GEMM if there are any active elements
if (any_active) {
} else {
// Sparse path: mixed sparsity pattern, load data and compute with mask awareness
cute::copy(smem_tiled_copy_B, tCsB(_, _, _0{}), tCrB_copy_view(_, _, _0{}));
#pragma unroll
for (int i = 0; i < size<2>(tCrA); ++i) {
if (i < size<2>(tCrA) - 1) {
cute::copy(smem_tiled_copy_B, tCsB(_, _, i + 1), tCrB_copy_view(_, _, i + 1));
}
// Mixed sparse computation - some Tensor Cores utilized, mask will handle fine-grained sparsity
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
}
}
Expand Down
37 changes: 37 additions & 0 deletions docs/integration.md
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Original file line number Diff line number Diff line change
Expand Up @@ -739,6 +739,43 @@ __forceinline__ __device__ void sparse_gemm_impl(
2. **Register Allocation**: Critical masking operations performed in registers to minimize memory traffic
3. **Coalesced Access**: Memory access patterns optimized for GPU memory hierarchy
4. **Template Specialization**: Compile-time optimization eliminates runtime branching
5. **Block-Level Sparse Optimization**: Advanced sparsity analysis with early branching and active block batching

#### Block-Level Sparse GEMM Optimizations

The optimized sparse GEMM implementation provides better Tensor Core utilization through:

**Approach 1: Early Branching**
- Analyzes sparsity patterns at MMA block granularity before computation
- Branches computation into three optimized paths:
- **Dense Path**: All MMA blocks active → Full Tensor Core utilization
- **Sparse Path**: Mixed sparsity → Selective computation with mask handling
- **Empty Path**: No active blocks → Skip computation entirely

**Approach 2: Active Block Batching**
- Pre-counts active MMA blocks requiring computation
- Optimizes memory loading based on sparsity density
- Reduces unnecessary data movement for fully masked regions

```cpp
// Optimized sparse GEMM with block-level analysis
if (active_block_count == 0) {
// Empty path: Skip all computation, clear registers
return;
} else if (active_block_count == num_mma_blocks) {
// Dense path: Full Tensor Core utilization
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
} else {
// Sparse path: Mixed computation with mask awareness
cute::gemm(tiled_mma, tCrA(_, _, i), tCrB(_, _, i), acc);
}
```

**Benefits:**
- Better Tensor Core utilization for structured sparse patterns
- Reduced computation overhead for sparse blocks
- Maintains warp coherency while enabling block-level optimization
- Compatible with existing mask application logic

## Memory Layout

Expand Down