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Warp specalization 362.

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This commit is contained in:
Tim Dettmers 2023-05-01 08:21:12 -07:00
parent cabcd9b9d5
commit 7cc8ff4727
3 changed files with 61 additions and 52 deletions
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102
csrc/kernels.cu
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@ -3041,7 +3041,7 @@ template <typename T, typename TCAST, int ITEMS> __device__ inline void vector_l
}
}
#define WARPS 2
#define WARPS 4
template <typename T, int BITS, int THREADS> __global__ void gemm_device(int M, int N, int K, T * __restrict__ const A, T* B, T * out, int lda, int ldb, int ldc)
{
@ -3056,17 +3056,18 @@ template <typename T, int BITS, int THREADS> __global__ void gemm_device(int M,
const int warp_id = threadIdx.x / 32;
const int half_warp_id = threadIdx.x / 16;
const int half_warp_lane = threadIdx.x % 16;
const int batch_size_warps = (WARPS-1)*2;
T local_A[1];
T local_B[8];
const int a_tile_offset = 32*16 + 16;
const int b_tile_offset = 16*8 + 16;
const int a_tile_offset = (32*16 + 16);
const int b_tile_offset = (16*8 + 16);
const int c_tile_offset = 32*8 + 24;
__shared__ T smem_A[WARPS*32*16 + (16*(WARPS-1))];
__shared__ T smem_B[WARPS*16*8 + (16*(WARPS-1))];
__shared__ T smem_C[WARPS*32*8 + (24*(WARPS-1))];
__shared__ T smem_A[2*batch_size_warps*32*16 + (2*16*(batch_size_warps-1))];
__shared__ T smem_B[2*batch_size_warps*16*8 + (2*16*(batch_size_warps-1))];
__shared__ T smem_C[32*8];
wmma::fragment<wmma::matrix_a, 32, 8, 16, half, wmma::row_major> a_frag;
wmma::fragment<wmma::matrix_b, 32, 8, 16, half, wmma::col_major> b_frag;
@ -3091,63 +3092,68 @@ template <typename T, int BITS, int THREADS> __global__ void gemm_device(int M,
//int block_idx = 0;
//for(int base_idx = 0; base_idx < K; base_idx+=blockDim.x)
for(int base_idx = 0; base_idx < K; base_idx+=blockDim.x)
int ticktock = 0;
int idx = 0 + threadIdx.x;
// prefetch
if(idx < K && warp_id < (WARPS-1))
{
int idx = base_idx + threadIdx.x;
local_A[0] = A[idx];
for(int k = 0; k < 2; k++)
#pragma unroll 8
for(int col = 0; col < 8; col++)
local_B[col] = B[(col_offset+col)*ldb+idx];
smem_A[half_warp_lane + (half_warp_id*a_tile_offset)] = local_A[0];
#pragma unroll 8
for(int col = 0; col < 8; col++)
smem_B[half_warp_lane + (half_warp_id*b_tile_offset) + (col*16)] = local_B[col];
}
ticktock = ticktock == 0 ? 1 : 0;
for(int base_idx = 0; base_idx < K; base_idx+=blockDim.x-32)
{
idx = base_idx + threadIdx.x;
__syncthreads();
if(idx < K && warp_id < (WARPS-1))
{
if(k == 0)
{
if(idx < K)
{
local_A[0] = A[idx];
local_A[0] = A[idx];
#pragma unroll 8
for(int col = 0; col < 8; col++)
local_B[col] = B[(col_offset+col)*ldb+idx];
}
#pragma unroll 8
for(int col = 0; col < 8; col++)
local_B[col] = B[(col_offset+col)*ldb+idx];
}
smem_A[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*a_tile_offset)] = local_A[0];
if(idx >= K)
{
smem_A[threadIdx.x] = 0.0f;
//smem_B[threadIdx.x] = 0.0f;
}
else
{
if((k == 0 && half_warp_id % 2 == 0) ||
(k == 1 && half_warp_id % 2 == 1))
{
smem_A[half_warp_lane + (warp_id*a_tile_offset)] = local_A[0];
#pragma unroll 8
for(int col = 0; col < 8; col++)
smem_B[half_warp_lane + (warp_id*b_tile_offset) + (col*16)] = local_B[col];
}
}
__syncthreads();
wmma::load_matrix_sync(a_frag, &(smem_A[warp_id*a_tile_offset]), 16); // 111 mu
wmma::load_matrix_sync(b_frag, &(smem_B[warp_id*b_tile_offset]), 16); // 35 mu
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
#pragma unroll 8
for(int col = 0; col < 8; col++)
smem_B[half_warp_lane + (((batch_size_warps*ticktock)+half_warp_id)*b_tile_offset) + (col*16)] = local_B[col];
}
ticktock = ticktock == 0 ? 1 : 0;
if(warp_id == (WARPS-1))
for(int k = 0; k < batch_size_warps; k++)
{
wmma::load_matrix_sync(a_frag, &(smem_A[(ticktock*batch_size_warps + k)*a_tile_offset]), 16); // 111 mu
wmma::load_matrix_sync(b_frag, &(smem_B[(ticktock*batch_size_warps + k)*b_tile_offset]), 16); // 35 mu
wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);
}
}
// 129 mu
wmma::store_matrix_sync(&(smem_C[half_warp_id*c_tile_offset]), c_frag, 8, wmma::mem_row_major);
if(warp_id == (WARPS-1))
wmma::store_matrix_sync(&(smem_C[0]), c_frag, 8, wmma::mem_row_major);
__syncthreads();
//if(threadIdx.x >= 16){ return; }
//printf("%i %f\n", threadIdx.x, (float)smem_C[threadIdx.x]);
//if(threadIdx.x < 32)
if(half_warp_lane < 8 && half_warp_id > 0)
//local_C[warp_lane] = smem_C[warp_lane + (warp_id*32*8)];
atomicAdd(&(smem_C[half_warp_lane]), smem_C[half_warp_lane + (half_warp_id*c_tile_offset)]);
__syncthreads();
//if(half_warp_lane < 8 && half_warp_id > 0)
// //local_C[warp_lane] = smem_C[warp_lane + (warp_id*32*8)];
// atomicAdd(&(smem_C[half_warp_lane]), smem_C[half_warp_lane + (half_warp_id*c_tile_offset)]);
//__syncthreads();
//local_accC[row] = BlockReduce(temp_storage.reduce).Reduce(local_accC[row], cub::Sum());
//if(threadIdx.x == 0)
@ -3463,6 +3469,7 @@ __global__ void with_staging_unified(float const* global_in, float * global_out,
// these are not used and make no sense, but the compiler needs them
//template __global__ void gemm_device<float, 16, 128>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 256>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 128>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
//template __global__ void gemm_device<float, 16, 32>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 32, 32>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
@ -3470,6 +3477,7 @@ template __global__ void gemm_device<half, 32, 64>(int M, int N, int K, half * _
// these are not used and make no sense, but the compiler needs them
//template __global__ void gemm_device<float, 32, 128>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 256>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 128>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);
//template __global__ void gemm_device<float, 32, 32>(int M, int N, int K, float * __restrict__ const A, float* B, float * out, int lda, int ldb, int ldc);
template __global__ void gemm_device<half, 16, 32>(int M, int N, int K, half * __restrict__ const A, half* B, half * out, int lda, int ldb, int ldc);

5
csrc/ops.cu
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@ -692,9 +692,10 @@ template <typename T> void gemm_host(int m, int n, int k, T * A, T* B, T * out
//gemm_device<T, 32, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 32, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
if(bits == 16)
//gemm_device<T, 16, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 256><<< num_blocks, 256, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
gemm_device<T, 16, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
gemm_device<T, 16, 64><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
//gemm_device<T, 16, 64><<< num_blocks, 64, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
}
template <typename T> void gemm_4bit_inference(int m, int n, int k, T * A, unsigned char* B, float *absmax, T * out, int lda, int ldb, int ldc, int blocksize)

6
tests/test_functional.py
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@ -2370,10 +2370,10 @@ def test_cutlass3_gemm(dtype):
C1 = torch.matmul(A, B.t())
C2 = F.cutlass3_gemm(A, B.t())
#print(C1)
#print(C2)
print(C1)
print(C2)
torch.testing.assert_close(C1, C2, atol=1e-05, rtol=0.05)
torch.testing.assert_close(C1, C2, atol=1e-05, rtol=0.06)
#@pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=['fp32', 'fp16'])
@pytest.mark.parametrize("dtype", [torch.float16], ids=['fp16'])