drbh

Migrated from kernels-community/flash-mla

0b53d3a unverified 15 days ago

9.6 kB

	// Adapted from https://github.com/Dao-AILab/flash-attention/blob/main/csrc/flash_attn/src/softmax.h

	#pragma once

	#include <cmath>

	#include <cute/tensor.hpp>
	#include <cutlass/numeric_types.h>

	#include "utils.h"

	namespace flash {

	using namespace cute;

	////////////////////////////////////////////////////////////////////////////////////////////////////

	template<bool zero_init=true, typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
	__device__ __forceinline__ void thread_reduce_(Tensor<Engine0, Layout0> const &tensor, Tensor<Engine1, Layout1> &summary, Operator &op) {
	static_assert(Layout0::rank == 2, "Only support 2D Tensor");
	static_assert(Layout1::rank == 1, "Only support 1D Tensor");
	CUTE_STATIC_ASSERT_V(size<0>(summary) == size<0>(tensor));
	#pragma unroll
	for (int mi = 0; mi < size<0>(tensor); mi++) {
	summary(mi) = zero_init ? tensor(mi, 0) : op(summary(mi), tensor(mi, 0));
	#pragma unroll
	for (int ni = 1; ni < size<1>(tensor); ni++) {
	summary(mi) = op(summary(mi), tensor(mi, ni));
	}
	}
	}

	template<typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
	__device__ __forceinline__ void quad_allreduce_(Tensor<Engine0, Layout0> &dst, Tensor<Engine1, Layout1> &src, Operator &op) {
	CUTE_STATIC_ASSERT_V(size(dst) == size(src));
	#pragma unroll
	for (int i = 0; i < size(dst); i++){
	dst(i) = Allreduce<4>::run(src(i), op);
	}
	}

	template<bool zero_init=true, typename Engine0, typename Layout0, typename Engine1, typename Layout1, typename Operator>
	__device__ __forceinline__ void reduce_(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1> &summary, Operator &op) {
	thread_reduce_<zero_init>(tensor, summary, op);
	quad_allreduce_(summary, summary, op);
	}

	template<bool zero_init=true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
	__device__ __forceinline__ void reduce_max(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1> &max){
	MaxOp<float> max_op;
	reduce_<zero_init>(tensor, max, max_op);
	}

	template<bool zero_init=true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
	__device__ __forceinline__ void reduce_sum(Tensor<Engine0, Layout0> const& tensor, Tensor<Engine1, Layout1> &sum){
	SumOp<float> sum_op;
	thread_reduce_<zero_init>(tensor, sum, sum_op);
	}

	// Apply the exp to all the elements.
	template <bool Scale_max=true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
	__forceinline__ __device__ auto scale_apply_exp2(Tensor<Engine0, Layout0> &tensor, Tensor<Engine1, Layout1> const &max, const float scale) {
	static_assert(Layout0::rank == 2, "Only support 2D Tensor");
	static_assert(Layout1::rank == 1, "Only support 1D Tensor");
	CUTE_STATIC_ASSERT_V(size<0>(max) == size<0>(tensor));
	#pragma unroll
	for (int mi = 0; mi < size<0>(tensor); ++mi) {
	// If max is -inf, then all elements must have been -inf (possibly due to masking).
	// We don't want (-inf - (-inf)) since that would give NaN.
	// If we don't have float around M_LOG2E the multiplication is done in fp64.
	const float max_scaled = max(mi) == -INFINITY ? 0.f : max(mi) * (Scale_max ? scale : float(M_LOG2E));
	#pragma unroll
	for (int ni = 0; ni < size<1>(tensor); ++ni) {
	// Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
	// max * log_2(e)) This allows the compiler to use the ffma
	// instruction instead of fadd and fmul separately.
	// The following macro will disable the use of fma.
	// See: https://github.com/pytorch/pytorch/issues/121558 for more details
	// This macro is set in PyTorch and not FlashAttention
	#ifdef UNFUSE_FMA
	tensor(mi, ni) = exp2f(__fmul_rn(tensor(mi, ni), scale) - max_scaled);
	#else
	tensor(mi, ni) = exp2f(tensor(mi, ni) * scale - max_scaled);
	#endif
	}
	}
	return tensor;
	}

	// Apply the exp to all the elements.
	template <bool zero_init=true, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
	__forceinline__ __device__ void max_scale_exp2_sum(Tensor<Engine0, Layout0> &tensor, Tensor<Engine1, Layout1> &max, Tensor<Engine1, Layout1> &sum, const float scale) {
	static_assert(Layout0::rank == 2, "Only support 2D Tensor");
	static_assert(Layout1::rank == 1, "Only support 1D Tensor");
	CUTE_STATIC_ASSERT_V(size<0>(max) == size<0>(tensor));
	#pragma unroll
	for (int mi = 0; mi < size<0>(tensor); ++mi) {
	MaxOp<float> max_op;
	max(mi) = zero_init ? tensor(mi, 0) : max_op(max(mi), tensor(mi, 0));
	#pragma unroll
	for (int ni = 1; ni < size<1>(tensor); ni++) {
	max(mi) = max_op(max(mi), tensor(mi, ni));
	}
	max(mi) = Allreduce<4>::run(max(mi), max_op);
	// If max is -inf, then all elements must have been -inf (possibly due to masking).
	// We don't want (-inf - (-inf)) since that would give NaN.
	const float max_scaled = max(mi) == -INFINITY ? 0.f : max(mi) * scale;
	sum(mi) = 0;
	#pragma unroll
	for (int ni = 0; ni < size<1>(tensor); ++ni) {
	// Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
	// max * log_2(e)) This allows the compiler to use the ffma
	// instruction instead of fadd and fmul separately.
	tensor(mi, ni) = exp2f(tensor(mi, ni) * scale - max_scaled);
	sum(mi) += tensor(mi, ni);
	}
	SumOp<float> sum_op;
	sum(mi) = Allreduce<4>::run(sum(mi), sum_op);
	}
	}

	template<typename Tensor0, typename Tensor1>
	__forceinline__ __device__ void rescale_o(Tensor0 &acc_o, Tensor1 &scale_o) {
	// Reshape acc_s from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
	Tensor acc_o_rowcol = make_tensor(acc_o.data(), flash::convert_layout_acc_rowcol(acc_o.layout()));
	#pragma unroll
	for (int mi = 0; mi < size(scale_o); ++mi) {
	#pragma unroll
	for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) { acc_o_rowcol(mi, ni) *= scale_o(mi); }
	}
	}

	////////////////////////////////////////////////////////////////////////////////////////////////////

	template <int kNRows>
	struct Softmax {

	using TensorT = decltype(make_tensor<float>(Shape<Int<kNRows>>{}));
	TensorT row_max, row_sum;

	__forceinline__ __device__ Softmax() {};

	template<bool Is_first, bool Check_inf=false, typename Tensor0>
	__forceinline__ __device__ TensorT softmax(Tensor0 &acc_s, float softmax_scale_log2) {
	// Reshape acc_s from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
	Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
	static_assert(decltype(size<0>(scores))::value == kNRows);
	TensorT scale_o;
	clear(scale_o);
	if (Is_first) {
	flash::template reduce_max</zero_init=/true>(scores, row_max);
	flash::scale_apply_exp2(scores, row_max, softmax_scale_log2);
	flash::reduce_sum</zero_init=/true>(scores, row_sum);
	} else {
	Tensor scores_max_prev = make_fragment_like(row_max);
	cute::copy(row_max, scores_max_prev);
	flash::template reduce_max</zero_init=/false>(scores, row_max);
	// Reshape acc_o from (MMA=4, MMA_M, MMA_K) to (nrow=(2, MMA_M), ncol=(2, MMA_K))
	#pragma unroll
	for (int mi = 0; mi < size(row_max); ++mi) {
	float scores_max_cur = !Check_inf
	? row_max(mi)
	: (row_max(mi) == -INFINITY ? 0.0f : row_max(mi));
	float scores_scale = exp2f((scores_max_prev(mi) - scores_max_cur) * softmax_scale_log2);
	scale_o(mi) = scores_scale;
	row_sum(mi) *= scores_scale;
	}
	flash::scale_apply_exp2(scores, row_max, softmax_scale_log2);
	// We don't do the reduce across threads here since we don't need to use the row_sum.
	// We do that reduce at the end when we need to normalize the softmax.
	flash::reduce_sum</zero_init=/false>(scores, row_sum);
	}
	return scale_o;
	};

	template<bool Is_dropout=false, bool Split=false, typename Tensor0>
	__forceinline__ __device__ TensorT normalize_softmax_lse(Tensor0 &acc_o, float softmax_scale, float rp_dropout=1.0) {
	SumOp<float> sum_op;
	quad_allreduce_(row_sum, row_sum, sum_op);
	TensorT lse = make_fragment_like(row_sum);
	// Reshape acc_s from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
	Tensor acc_o_rowcol = make_tensor(acc_o.data(), flash::convert_layout_acc_rowcol(acc_o.layout()));
	static_assert(decltype(size<0>(acc_o_rowcol))::value == kNRows);
	#pragma unroll
	for (int mi = 0; mi < size<0>(acc_o_rowcol); ++mi) {
	float sum = row_sum(mi);
	float inv_sum = (sum == 0.f \|\| sum != sum) ? 1.f : 1.f / sum;
	lse(mi) = (sum == 0.f \|\| sum != sum) ? (Split ? -INFINITY : INFINITY) : row_max(mi) * softmax_scale + __logf(sum);
	float scale = !Is_dropout ? inv_sum : inv_sum * rp_dropout;
	#pragma unroll
	for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) { acc_o_rowcol(mi, ni) *= scale; }
	}
	return lse;
	};
	};

	} // namespace flash