diff options
Diffstat (limited to 'candle-flash-attn/kernels/dropout.h')
| -rw-r--r-- | candle-flash-attn/kernels/dropout.h | 94 |
1 files changed, 94 insertions, 0 deletions
diff --git a/candle-flash-attn/kernels/dropout.h b/candle-flash-attn/kernels/dropout.h new file mode 100644 index 00000000..4882f97d --- /dev/null +++ b/candle-flash-attn/kernels/dropout.h @@ -0,0 +1,94 @@ +/****************************************************************************** + * Copyright (c) 2024, Tri Dao. + ******************************************************************************/ + +#pragma once + +#include "philox.cuh" +#include "utils.h" + +namespace flash { + +struct Dropout { + + const unsigned long long seed, offset; + const uint8_t p_dropout_in_uint8_t; + + __forceinline__ __device__ Dropout(const unsigned long long seed, const unsigned long long offset, + const uint8_t p_dropout_in_uint8_t, + const int bid, const int hid, const int tid, const int nheads) + : seed(seed) + , offset(offset + (bid * nheads + hid) * 32 + tid % 32) + , p_dropout_in_uint8_t(p_dropout_in_uint8_t) { + } + + template <bool encode_dropout_in_sign_bit=false, typename Engine, typename Layout> + __forceinline__ __device__ void apply_dropout(Tensor<Engine, Layout> &tensor_, + int block_row_start, int block_col_start, int block_row_stride) { + // convert shape from (4, MMA_M, MMA_N) to (8, MMA_M, MMA_N / 2) + Tensor tensor = make_tensor(tensor_.data(), flash::convert_layout_acc_dropout(tensor_.layout())); + using T = typename Engine::value_type; + auto encode_dropout = [](bool keep, T val) { + return keep ? val : (encode_dropout_in_sign_bit ? -val : T(0)); + }; + static_assert(decltype(size<2>(tensor))::value % 2 == 0); + const uint16_t p_dropout_8bit_in_uint16_t = uint16_t(p_dropout_in_uint8_t); + const uint32_t p_dropout_8bit_in_uint32_t = (uint32_t(p_dropout_8bit_in_uint16_t) << 16) | uint32_t(p_dropout_8bit_in_uint16_t); + // if (cute::thread0()) { printf("threshold2 = 0x%x\n", p_dropout_8bit_in_uint32_t); } + #pragma unroll + for (int m = 0; m < size<1>(tensor); ++m, block_row_start += block_row_stride) { + uint2 rowcol = make_uint2(block_row_start, block_col_start); + #pragma unroll + for (int n = 0; n < size<2>(tensor) / 2; ++n, ++rowcol.y) { + // if (cute::thread(32, 0)) { printf("m = %d, n = %d, row = %d, col = %d\n", m, n, int(rowcol.x), int(rowcol.y));} + uint4 random_uint4 = flash::philox(seed, reinterpret_cast<unsigned long long&>(rowcol), offset); + // if (cute::thread0()) { printf("philox = %u, %d, %d, %d\n", random_uint4.x, random_uint4.y, random_uint4.z, random_uint4.w);} + uint8_t (&rnd_8)[16] = reinterpret_cast<uint8_t (&)[16]>(random_uint4); + // Special implementation for 16-bit types: we duplicate the threshold to the + // low and high 16 bits of a 32-bit value, then use the f16x2 comparison instruction + // to get a mask. The low 16 bits of the mask will be either 0xffff or 0x0000, + // and the high 16 bits will be either 0xffff or 0x0000, depending on whether + // the random value is less than the threshold. + // We then do a bit-wise AND between the mask and the original value (in 32-bit). + // We're exploiting the fact that floating point comparison is equivalent to integer + // comparison, since we're comparing unsigned integers whose top 8-bits are zero. + if (!encode_dropout_in_sign_bit + && (std::is_same<T, cutlass::half_t>::value || std::is_same<T, cutlass::bfloat16_t>::value)) { + uint16_t rnd_16[16]; + #pragma unroll + for (int i = 0; i < 16; i++) { rnd_16[i] = uint16_t(rnd_8[i]); } + uint32_t (&rnd_32)[8] = reinterpret_cast<uint32_t (&)[8]>(rnd_16); + #pragma unroll + for (int j = 0; j < 2; j++) { + Tensor tensor_uint32 = recast<uint32_t>(tensor(_, m, n * 2 + j)); + // if (cute::thread0()) { printf("random = 0x%x, 0x%x, 0x%x, 0x%x\n", rnd_32[j * 4 + 0], rnd_32[j * 4 + 1], rnd_32[j * 4 + 2], rnd_32[j * 4 + 3]); } + // if (cute::thread0()) { printf("tensor_uint32 = 0x%x, 0x%x, 0x%x, 0x%x\n", tensor_uint32(0), tensor_uint32(1), tensor_uint32(2), tensor_uint32(3)); } + #pragma unroll + for (int i = 0; i < 4; i++) { + uint32_t mask; + asm volatile("set.le.u32.f16x2 %0, %1, %2;\n" : "=r"(mask) : "r"(rnd_32[j * 4 + i]), "r"(p_dropout_8bit_in_uint32_t)); + tensor_uint32(i) &= mask; + } + // if (cute::thread0()) { printf("tensor_uint32 = 0x%x, 0x%x, 0x%x, 0x%x\n", tensor_uint32(0), tensor_uint32(1), tensor_uint32(2), tensor_uint32(3)); } + } + } else { + #pragma unroll + for (int j = 0; j < 2; j++) { + #pragma unroll + for (int i = 0; i < 8; i++) { + tensor(i, m, n * 2 + j) = encode_dropout(rnd_8[j * 8 + i] <= p_dropout_in_uint8_t, tensor(i, m, n * 2 + j)); + } + Tensor tensor_uint32 = recast<uint32_t>(tensor(_, m, n * 2 + j)); + // if (cute::thread0()) { printf("tensor_uint32 = 0x%x, 0x%x, 0x%x, 0x%x\n", tensor_uint32(0), tensor_uint32(1), tensor_uint32(2), tensor_uint32(3)); } + } + } + // // if ((threadIdx.x == 0) && (blockIdx.x == 0) && (blockIdx.y == 0)) { + // // printf("n = %d, ph Philox: %u, %u, %u, %u\n", n, rnd_8.x, rnd_8.y, rnd_8.z, rnd_8.w); + // // } + } + } + } + +}; + +} // namespace flash |