Skip to content

GitLab

Commit 47ad0761 authored by Woosuk Kwon's avatar Woosuk Kwon
Browse files

Remove backward pass

parent 06c23892
Hide whitespace changes
Inline Side-by-side
Showing with 2 additions and 2256 deletions
csrc/flash_attn/flash_api.cpp
View file @ 47ad0761
......@@ -137,83 +137,6 @@ void set_params_fprop(Flash_fwd_params &params,
#endif
}
void set_params_dgrad(Flash_bwd_params &params,
// sizes
const size_t b,
const size_t seqlen_q,
const size_t seqlen_k,
const size_t seqlen_q_rounded,
const size_t seqlen_k_rounded,
const size_t h,
const size_t h_k,
const size_t d,
const size_t d_rounded,
// device pointers
const at::Tensor q,
const at::Tensor k,
const at::Tensor v,
const at::Tensor out,
const at::Tensor dout,
at::Tensor dq,
at::Tensor dk,
at::Tensor dv,
void *cu_seqlens_q_d,
void *cu_seqlens_k_d,
void *dq_accum_d,
void *dk_accum_d,
void *dv_accum_d,
void *softmax_lse_d,
void *dsoftmax_sum_d,
float p_dropout,
float softmax_scale,
int window_size_left,
int window_size_right,
bool deterministic) {
set_params_fprop(params,
b, seqlen_q, seqlen_k, seqlen_q_rounded, seqlen_k_rounded, h, h_k, d, d_rounded,
q, k, v, out,
cu_seqlens_q_d,
cu_seqlens_k_d,
nullptr,
nullptr,
softmax_lse_d,
p_dropout,
softmax_scale,
window_size_left,
window_size_right);
// Set the pointers and strides.
params.do_ptr = dout.data_ptr();
params.do_row_stride = dout.stride(-3);
params.do_head_stride = dout.stride(-2);
params.dq_ptr = dq.data_ptr();
params.dk_ptr = dk.data_ptr();
params.dv_ptr = dv.data_ptr();
params.dq_row_stride = dq.stride(-3);
params.dk_row_stride = dk.stride(-3);
params.dv_row_stride = dv.stride(-3);
params.dq_head_stride = dq.stride(-2);
params.dk_head_stride = dk.stride(-2);
params.dv_head_stride = dv.stride(-2);
if (cu_seqlens_q_d == nullptr) {
params.do_batch_stride = dout.stride(0);
params.dq_batch_stride = dq.stride(0);
params.dk_batch_stride = dk.stride(0);
params.dv_batch_stride = dv.stride(0);
}
params.dq_accum_ptr = dq_accum_d;
params.dk_accum_ptr = dk_accum_d;
params.dv_accum_ptr = dv_accum_d;
// Softmax sum
params.dsoftmax_sum = dsoftmax_sum_d;
params.deterministic = deterministic;
}
void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream, bool force_split_kernel=false) {
FP16_SWITCH(!params.is_bf16, [&] {
HEADDIM_SWITCH(params.d, [&] {
......@@ -737,490 +660,6 @@ mha_varlen_fwd(at::Tensor &q, // total_q x num_heads x head_size, total_q := \s
return {out, q_padded, k_padded, v_padded, out_padded, softmax_lse, p, rng_state};
}
void run_mha_bwd(Flash_bwd_params &params, cudaStream_t stream) {
FP16_SWITCH(!params.is_bf16, [&] {
HEADDIM_SWITCH(params.d, [&] {
run_mha_bwd_<elem_type, kHeadDim>(params, stream);
});
});
}
std::vector<at::Tensor>
mha_bwd(const at::Tensor &dout, // batch_size x seqlen_q x num_heads, x head_size_og
const at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
const at::Tensor &k, // batch_size x seqlen_k x num_heads_k x head_size
const at::Tensor &v, // batch_size x seqlen_k x num_heads_k x head_size
const at::Tensor &out, // batch_size x seqlen_q x num_heads x head_size
const at::Tensor &softmax_lse, // b x h x seqlen_q
c10::optional<at::Tensor> &dq_, // batch_size x seqlen_q x num_heads x head_size
c10::optional<at::Tensor> &dk_, // batch_size x seqlen_k x num_heads_k x head_size
c10::optional<at::Tensor> &dv_, // batch_size x seqlen_k x num_heads_k x head_size
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or batch_size x num_heads
const float p_dropout, // probability to drop
const float softmax_scale,
const bool is_causal,
int window_size_left,
int window_size_right,
const bool deterministic,
c10::optional<at::Generator> gen_,
c10::optional<at::Tensor> &rng_state) {
#ifdef FLASHATTENTION_DISABLE_BACKWARD
TORCH_CHECK(false, "This flash attention build does not support backward.");
#endif
if (is_causal) { window_size_right = 0; }
auto dprops = at::cuda::getCurrentDeviceProperties();
// bool is_sm75 = dprops->major == 7 && dprops->minor == 5;
bool is_sm8x = dprops->major == 8 && dprops->minor >= 0;
bool is_sm80 = dprops->major == 8 && dprops->minor == 0;
bool is_sm90 = dprops->major == 9 && dprops->minor == 0;
TORCH_CHECK(is_sm90 || is_sm8x, "FlashAttention only supports Ampere GPUs or newer.");
// We will support Turing in the near future
// TORCH_CHECK(is_sm90 || is_sm8x || is_sm75, "FlashAttention only supports Turing GPUs or newer.");
bool is_dropout = p_dropout > 0.0;
auto stream = at::cuda::getCurrentCUDAStream().stream();
auto q_dtype = q.dtype();
TORCH_CHECK(q_dtype == torch::kFloat16 || q_dtype == torch::kBFloat16,
"FlashAttention only support fp16 and bf16 data type");
if (q_dtype == torch::kBFloat16) {
TORCH_CHECK(is_sm90 || is_sm8x, "bfloat16 is only supported on Ampere GPUs or newer");
}
TORCH_CHECK(k.dtype() == q_dtype, "query and key must have the same dtype");
TORCH_CHECK(v.dtype() == q_dtype, "query and value must have the same dtype");
TORCH_CHECK(out.dtype() == q_dtype, "query and out must have the same dtype");
TORCH_CHECK(dout.dtype() == q_dtype, "query and dout must have the same dtype");
CHECK_DEVICE(q); CHECK_DEVICE(k); CHECK_DEVICE(v);
CHECK_DEVICE(out); CHECK_DEVICE(dout); CHECK_DEVICE(softmax_lse);
TORCH_CHECK(q.stride(-1) == 1, "Input tensor must have contiguous last dimension");
TORCH_CHECK(k.stride(-1) == 1, "Input tensor must have contiguous last dimension");
TORCH_CHECK(v.stride(-1) == 1, "Input tensor must have contiguous last dimension");
TORCH_CHECK(out.stride(-1) == 1, "out tensor must have contiguous last dimension");
TORCH_CHECK(dout.stride(-1) == 1, "dout tensor must have contiguous last dimension");
const auto sizes = q.sizes();
const int batch_size = sizes[0];
const int seqlen_q = sizes[1];
const int num_heads = sizes[2];
const int head_size_og = dout.size(3);
const int head_size = sizes[3];
const int seqlen_k = k.size(1);
const int num_heads_k = k.size(2);
TORCH_CHECK(batch_size > 0, "batch size must be positive");
TORCH_CHECK(head_size % 8 == 0, "head_size should be a multiple of 8");
TORCH_CHECK(head_size <= 256, "FlashAttention backward only supports head dimension at most 256");
if (head_size > 192 && (head_size <= 224 || is_dropout)) {
TORCH_CHECK(is_sm80 || is_sm90, "FlashAttention backward for head dim 256 with dropout, or head dim 224 with/without dropout requires A100/A800 or H100/H800");
}
TORCH_CHECK(num_heads % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query");
auto round_multiple = [](int x, int m) { return (x + m - 1) / m * m; };
const int head_size_rounded = round_multiple(head_size, 32);
const int seqlen_q_rounded = round_multiple(seqlen_q, 128);
const int seqlen_k_rounded = round_multiple(seqlen_k, 128);
TORCH_CHECK(head_size == round_multiple(head_size_og, 8), "head_size must be head_size_og rounded to a multiple of 8");
if (window_size_left >= seqlen_k) { window_size_left = -1; }
if (window_size_right >= seqlen_k) { window_size_right = -1; }
CHECK_SHAPE(q, batch_size, seqlen_q, num_heads, head_size);
CHECK_SHAPE(k, batch_size, seqlen_k, num_heads_k, head_size);
CHECK_SHAPE(v, batch_size, seqlen_k, num_heads_k, head_size);
CHECK_SHAPE(out, batch_size, seqlen_q, num_heads, head_size);
CHECK_SHAPE(dout, batch_size, seqlen_q, num_heads, head_size_og);
at::Tensor dq, dk, dv;
if (dq_.has_value()) {
dq = dq_.value();
TORCH_CHECK(dq.dtype() == q_dtype, "dq must have the same dtype as q");
CHECK_DEVICE(dq);
TORCH_CHECK(dq.stride(-1) == 1, "dq must have contiguous last dimension");
CHECK_SHAPE(dq, batch_size, seqlen_q, num_heads, head_size);
} else {
dq = torch::empty_like(q);
}
if (dk_.has_value()) {
dk = dk_.value();
TORCH_CHECK(dk.dtype() == q_dtype, "dk must have the same dtype as q");
CHECK_DEVICE(dk);
TORCH_CHECK(dk.stride(-1) == 1, "dk must have contiguous last dimension");
CHECK_SHAPE(dk, batch_size, seqlen_k, num_heads_k, head_size);
} else {
dk = torch::empty_like(k);
}
if (dv_.has_value()) {
dv = dv_.value();
TORCH_CHECK(dv.dtype() == q_dtype, "dv must have the same dtype as q");
CHECK_DEVICE(dv);
TORCH_CHECK(dv.stride(-1) == 1, "dv must have contiguous last dimension");
CHECK_SHAPE(dv, batch_size, seqlen_k, num_heads_k, head_size);
} else {
dv = torch::empty_like(v);
}
at::Tensor dout_padded;
if (head_size_og % 8 != 0) {
dout_padded = torch::nn::functional::pad(dout, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
} else {
dout_padded = dout;
}
// bool loop = seqlen_k > blocksize_c;
// TODO: change later, for now set to true for simplicity
bool loop = true;
// Otherwise the kernel will be launched from cuda:0 device
// Cast to char to avoid compiler warning about narrowing
at::cuda::CUDAGuard device_guard{(char)q.get_device()};
auto opts = q.options();
auto softmax_d = torch::empty({batch_size, num_heads, seqlen_q_rounded}, opts.dtype(at::kFloat));
at::Tensor dq_accum;
at::Tensor dk_accum, dv_accum;
if (loop) {
if (!deterministic) {
dq_accum = torch::empty({batch_size, seqlen_q_rounded, num_heads, head_size_rounded}, opts.dtype(at::kFloat));
} else {
const int nsplits = (dprops->multiProcessorCount + batch_size * num_heads - 1) / (batch_size * num_heads);
dq_accum = torch::zeros({nsplits, batch_size, seqlen_q_rounded, num_heads, head_size_rounded}, opts.dtype(at::kFloat));
}
// dk_accum = torch::empty({batch_size, num_heads_k, seqlen_k_rounded, head_size_rounded}, opts.dtype(at::kFloat));
// dv_accum = torch::empty({batch_size, num_heads_k, seqlen_k_rounded, head_size_rounded}, opts.dtype(at::kFloat));
}
at::Tensor dk_expanded, dv_expanded;
if (num_heads_k != num_heads) { // MQA / GQA
dk_expanded = torch::empty({batch_size, seqlen_k, num_heads, head_size}, opts);
dv_expanded = torch::empty({batch_size, seqlen_k, num_heads, head_size}, opts);
} else {
dk_expanded = dk;
dv_expanded = dv;
}
Flash_bwd_params params;
set_params_dgrad(params,
batch_size,
seqlen_q, seqlen_k,
seqlen_q_rounded, seqlen_k_rounded,
num_heads, num_heads_k,
head_size, head_size_rounded,
q, k, v, out,
dout_padded, dq, dk_expanded, dv_expanded,
nullptr,
nullptr,
loop ? dq_accum.data_ptr() : nullptr,
// loop ? dk_accum.data_ptr() : nullptr,
// loop ? dv_accum.data_ptr() : nullptr,
nullptr,
nullptr,
softmax_lse.data_ptr(),
softmax_d.data_ptr(),
p_dropout,
softmax_scale,
window_size_left,
window_size_right,
deterministic);
params.dq_accum_split_stride = !deterministic ? 0 : dq_accum.stride(0);
auto launch = &run_mha_bwd;
auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(
gen_, at::cuda::detail::getDefaultCUDAGenerator());
// We use a custom RNG that increases the offset by batch_size * nheads * 32.
int64_t counter_offset = params.b * params.h * 32;
if ( rng_state.has_value() ) {
params.rng_state = reinterpret_cast<uint64_t*>(rng_state.value().data_ptr());
} else if( is_dropout ) {
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(gen->mutex_);
params.philox_args = gen->philox_cuda_state(counter_offset);
auto seeds = at::cuda::philox::unpack(params.philox_args);
params.rng_state[0] = std::get<0>(seeds);
params.rng_state[1] = std::get<1>(seeds);
}
set_params_alibi(params, alibi_slopes_, batch_size, num_heads);
if (seqlen_q > 0) {
launch(params, stream);
} else {
// If seqlen_q == 0, then we have an empty tensor. We need to set the output to 0.
dk_expanded.zero_();
dv_expanded.zero_();
softmax_d.zero_();
}
// For MQA/GQA we need to sum dK and dV across the groups
if (num_heads_k != num_heads) {
at::sum_out(dk, at::reshape(dk_expanded, {batch_size, seqlen_k, num_heads_k, num_heads / num_heads_k, head_size}), {3});
at::sum_out(dv, at::reshape(dv_expanded, {batch_size, seqlen_k, num_heads_k, num_heads / num_heads_k, head_size}), {3});
}
if (head_size_og % 8 != 0) {
dq = dq.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
dk = dk.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
dv = dv.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
}
return { dq, dk, dv, softmax_d };
}
std::vector<at::Tensor>
mha_varlen_bwd(const at::Tensor &dout, // total_q x num_heads, x head_size
const at::Tensor &q, // total_q x num_heads x head_size, total_q := \sum_{i=0}^{b} s_i
const at::Tensor &k, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i
const at::Tensor &v, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i
const at::Tensor &out, // total_q x num_heads x head_size
const at::Tensor &softmax_lse, // b x h x s softmax logsumexp
c10::optional<at::Tensor> &dq_, // total_q x num_heads x head_size, total_q := \sum_{i=0}^{b} s_i
c10::optional<at::Tensor> &dk_, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i
c10::optional<at::Tensor> &dv_, // total_k x num_heads_k x head_size, total_k := \sum_{i=0}^{b} s_i
const at::Tensor &cu_seqlens_q, // b+1
const at::Tensor &cu_seqlens_k, // b+1
c10::optional<at::Tensor> &alibi_slopes_, // num_heads or b x num_heads
const int max_seqlen_q,
const int max_seqlen_k, // max sequence length to choose the kernel
const float p_dropout, // probability to drop
const float softmax_scale,
const bool zero_tensors,
const bool is_causal,
int window_size_left,
int window_size_right,
const bool deterministic,
c10::optional<at::Generator> gen_,
c10::optional<at::Tensor> &rng_state) {
#ifdef FLASHATTENTION_DISABLE_BACKWARD
TORCH_CHECK(false, "This flash attention build does not support backward.");
#endif
if (is_causal) { window_size_right = 0; }
auto dprops = at::cuda::getCurrentDeviceProperties();
// bool is_sm75 = dprops->major == 7 && dprops->minor == 5;
bool is_sm8x = dprops->major == 8 && dprops->minor >= 0;
bool is_sm80 = dprops->major == 8 && dprops->minor == 0;
bool is_sm90 = dprops->major == 9 && dprops->minor == 0;
TORCH_CHECK(is_sm90 || is_sm8x, "FlashAttention only supports Ampere GPUs or newer.");
// We will support Turing in the near future
// TORCH_CHECK(is_sm90 || is_sm8x || is_sm75, "FlashAttention only supports Turing GPUs or newer.");
bool is_dropout = p_dropout > 0.0;
auto stream = at::cuda::getCurrentCUDAStream().stream();
auto q_dtype = q.dtype();
TORCH_CHECK(q_dtype == torch::kFloat16 || q_dtype == torch::kBFloat16,
"FlashAttention only support fp16 and bf16 data type");
if (q_dtype == torch::kBFloat16) {
TORCH_CHECK(is_sm90 || is_sm8x, "bfloat16 is only supported on Ampere GPUs or newer");
}
TORCH_CHECK(k.dtype() == q_dtype, "query and key must have the same dtype");
TORCH_CHECK(v.dtype() == q_dtype, "query and value must have the same dtype");
TORCH_CHECK(out.dtype() == q_dtype, "query and out must have the same dtype");
TORCH_CHECK(dout.dtype() == q_dtype, "query and dout must have the same dtype");
TORCH_CHECK(cu_seqlens_q.dtype() == torch::kInt32, "cu_seqlens_q must have dtype int32");
TORCH_CHECK(cu_seqlens_k.dtype() == torch::kInt32, "cu_seqlens_k must have dtype int32");
CHECK_DEVICE(q); CHECK_DEVICE(k); CHECK_DEVICE(v);
CHECK_DEVICE(out); CHECK_DEVICE(dout); CHECK_DEVICE(softmax_lse);
CHECK_DEVICE(cu_seqlens_q); CHECK_DEVICE(cu_seqlens_k);
TORCH_CHECK(q.stride(-1) == 1, "Input tensor must have contiguous last dimension");
TORCH_CHECK(k.stride(-1) == 1, "Input tensor must have contiguous last dimension");
TORCH_CHECK(v.stride(-1) == 1, "Input tensor must have contiguous last dimension");
TORCH_CHECK(out.stride(-1) == 1, "out tensor must have contiguous last dimension");
TORCH_CHECK(dout.stride(-1) == 1, "dout tensor must have contiguous last dimension");
CHECK_CONTIGUOUS(cu_seqlens_q);
CHECK_CONTIGUOUS(cu_seqlens_k);
const auto sizes = q.sizes();
const int total_q = sizes[0];
const int batch_size = cu_seqlens_q.numel() - 1;
const int num_heads = sizes[1];
const int head_size_og = dout.size(2);
const int head_size = sizes[2];
const int total_k = k.size(0);
const int num_heads_k = k.size(1);
TORCH_CHECK(batch_size > 0, "batch size must be positive");
TORCH_CHECK(head_size % 8 == 0, "head_size should be a multiple of 8");
TORCH_CHECK(head_size <= 256, "FlashAttention backward only supports head dimension at most 256");
if (head_size > 192 && (head_size <= 224 || is_dropout)) {
TORCH_CHECK(is_sm80 || is_sm90, "FlashAttention backward for head dim 256 with dropout, or head dim 224 with/without dropout requires A100/A800 or H100/H800");
}
TORCH_CHECK(num_heads % num_heads_k == 0, "Number of heads in key/value must divide number of heads in query");
auto round_multiple = [](int x, int m) { return (x + m - 1) / m * m; };
const int head_size_rounded = round_multiple(head_size, 32);
const int seqlen_q_rounded = round_multiple(max_seqlen_q, 128);
const int seqlen_k_rounded = round_multiple(max_seqlen_k, 128);
TORCH_CHECK(head_size == round_multiple(head_size_og, 8), "head_size must be head_size_og rounded to a multiple of 8");
if (window_size_left >= max_seqlen_k) { window_size_left = -1; }
if (window_size_right >= max_seqlen_k) { window_size_right = -1; }
CHECK_SHAPE(q, total_q, num_heads, head_size);
CHECK_SHAPE(k, total_k, num_heads_k, head_size);
CHECK_SHAPE(v, total_k, num_heads_k, head_size);
CHECK_SHAPE(out, total_q, num_heads, head_size);
CHECK_SHAPE(dout, total_q, num_heads, head_size_og);
CHECK_SHAPE(cu_seqlens_q, batch_size + 1);
CHECK_SHAPE(cu_seqlens_k, batch_size + 1);
at::Tensor dq, dk, dv;
if (dq_.has_value()) {
dq = dq_.value();
TORCH_CHECK(dq.dtype() == q_dtype, "dq must have the same dtype as q");
CHECK_DEVICE(dq);
TORCH_CHECK(dq.stride(-1) == 1, "dq must have contiguous last dimension");
CHECK_SHAPE(dq, total_q, num_heads, head_size);
} else {
dq = torch::empty_like(q);
}
if (dk_.has_value()) {
dk = dk_.value();
TORCH_CHECK(dk.dtype() == q_dtype, "dk must have the same dtype as q");
CHECK_DEVICE(dk);
TORCH_CHECK(dk.stride(-1) == 1, "dk must have contiguous last dimension");
CHECK_SHAPE(dk, total_k, num_heads_k, head_size);
} else {
dk = torch::empty_like(k);
}
if (dv_.has_value()) {
dv = dv_.value();
TORCH_CHECK(dv.dtype() == q_dtype, "dv must have the same dtype as q");
CHECK_DEVICE(dv);
TORCH_CHECK(dv.stride(-1) == 1, "dv must have contiguous last dimension");
CHECK_SHAPE(dv, total_k, num_heads_k, head_size);
} else {
dv = torch::empty_like(v);
}
at::Tensor dout_padded;
if (head_size_og % 8 != 0) {
dout_padded = torch::nn::functional::pad(dout, torch::nn::functional::PadFuncOptions({0, 8 - head_size_og % 8}));
} else {
dout_padded = dout;
}
// bool loop = max_seqlen_k > blocksize_c;
// TODO: change later, for now set to true for simplicity
bool loop = true;
// Otherwise the kernel will be launched from cuda:0 device
// Cast to char to avoid compiler warning about narrowing
at::cuda::CUDAGuard device_guard{(char)q.get_device()};
auto opts = q.options();
auto softmax_d = torch::empty({batch_size, num_heads, seqlen_q_rounded}, opts.dtype(at::kFloat));
at::Tensor dq_accum;
if (loop) {
// We don't want to allocate dq_accum of size (batch, seqlen_q_rounded, num_heads, head_size_rounded)
// because that would be too large if there is a very long sequence and the rest of the sequences are short.
// Instead, we allocate dq_accum of size (total_q + 128 * batch, num_heads, head_size_rounded).
// Note that 128 is the max block size on the seqlen_q dimension.
// For dQ, the i-th sequence is stored in indices from cu_seqlens[i] + 128 * i to
// cu_seqlens[i + 1] * 128 * i - 1. This ensures that the i-th sequence and (i + 1)-th sequence will
// be at least 128 apart. It's ok for us to do atomicAdds up to 128 rows beyond what we're normally
// allowed to do. So we won't have to do any bound checking, and performance should stay the same.
if (!deterministic) {
dq_accum = torch::empty({total_q + 128 * batch_size, num_heads, head_size_rounded}, opts.dtype(at::kFloat));
} else {
const int nsplits = (dprops->multiProcessorCount + batch_size * num_heads - 1) / (batch_size * num_heads);
dq_accum = torch::zeros({nsplits, total_q + 128 * batch_size, num_heads, head_size_rounded}, opts.dtype(at::kFloat));
}
}
at::Tensor dk_expanded, dv_expanded;
if (num_heads_k != num_heads) { // MQA / GQA
dk_expanded = torch::empty({total_k, num_heads, head_size}, opts);
dv_expanded = torch::empty({total_k, num_heads, head_size}, opts);
} else {
dk_expanded = dk;
dv_expanded = dv;
}
if( zero_tensors ) {
dq.zero_();
dk_expanded.zero_();
dv_expanded.zero_();
softmax_d.zero_();
}
Flash_bwd_params params;
set_params_dgrad(params,
batch_size,
max_seqlen_q, max_seqlen_k,
seqlen_q_rounded, seqlen_k_rounded,
num_heads, num_heads_k,
head_size, head_size_rounded,
q, k, v, out,
dout_padded, dq, dk_expanded, dv_expanded,
cu_seqlens_q.data_ptr(),
cu_seqlens_k.data_ptr(),
loop ? dq_accum.data_ptr() : nullptr,
nullptr,
nullptr,
softmax_lse.data_ptr(),
softmax_d.data_ptr(),
p_dropout,
softmax_scale,
window_size_left,
window_size_right,
deterministic);
params.dq_accum_split_stride = !deterministic ? 0 : dq_accum.stride(0);
auto launch = &run_mha_bwd;
auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(
gen_, at::cuda::detail::getDefaultCUDAGenerator());
// We use a custom RNG that increases the offset by batch_size * nheads * 32.
int64_t counter_offset = params.b * params.h * 32;
if ( rng_state.has_value() ) {
params.rng_state = reinterpret_cast<uint64_t*>(rng_state.value().data_ptr());
} else if( is_dropout ) {
// See Note [Acquire lock when using random generators]
std::lock_guard<std::mutex> lock(gen->mutex_);
params.philox_args = gen->philox_cuda_state(counter_offset);
auto seeds = at::cuda::philox::unpack(params.philox_args);
params.rng_state[0] = std::get<0>(seeds);
params.rng_state[1] = std::get<1>(seeds);
}
set_params_alibi(params, alibi_slopes_, batch_size, num_heads);
if (max_seqlen_q > 0) {
launch(params, stream);
} else {
// If seqlen_q == 0, then we have an empty tensor. We need to set the output to 0.
dk_expanded.zero_();
dv_expanded.zero_();
softmax_d.zero_();
}
// For MQA/GQA we need to sum dK and dV across the groups
if (num_heads_k != num_heads) {
at::sum_out(dk, at::reshape(dk_expanded, {total_k, num_heads_k, num_heads / num_heads_k, head_size}), {2});
at::sum_out(dv, at::reshape(dv_expanded, {total_k, num_heads_k, num_heads / num_heads_k, head_size}), {2});
}
if (head_size_og % 8 != 0) {
dq = dq.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
dk = dk.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
dv = dv.index({"...", torch::indexing::Slice(torch::indexing::None, head_size_og)});
}
return { dq, dk, dv, softmax_d };
}
std::vector<at::Tensor>
mha_fwd_kvcache(at::Tensor &q, // batch_size x seqlen_q x num_heads x head_size
const at::Tensor &kcache, // batch_size_c x seqlen_k x num_heads_k x head_size or num_blocks x page_block_size x num_heads_k x head_size if there's a block_table.
......@@ -1493,7 +932,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.doc() = "FlashAttention";
m.def("fwd", &mha_fwd, "Forward pass");
m.def("varlen_fwd", &mha_varlen_fwd, "Forward pass (variable length)");
m.def("bwd", &mha_bwd, "Backward pass");
m.def("varlen_bwd", &mha_varlen_bwd, "Backward pass (variable length)");
// m.def("bwd", &mha_bwd, "Backward pass");
// m.def("varlen_bwd", &mha_varlen_bwd, "Backward pass (variable length)");
m.def("fwd_kvcache", &mha_fwd_kvcache, "Forward pass, with KV-cache");
}
csrc/flash_attn/src/flash_bwd_hdim128_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 128>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim128<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim128_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 128>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim128<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim160_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 160>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim160<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim160_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 160>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim160<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim192_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 192>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim192<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim192_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 192>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim192<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim224_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 224>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim224<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim224_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 224>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim224<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim256_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 256>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim256<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim256_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 256>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim256<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim32_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 32>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim32<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim32_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 32>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim32<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim64_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 64>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim64<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim64_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 64>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim64<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim96_bf16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::bfloat16_t, 96>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim96<cutlass::bfloat16_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_hdim96_fp16_sm80.cu deleted 100644 → 0
View file @ 06c23892
// Copyright (c) 2023, Tri Dao.
// Splitting the different head dimensions to different files to speed up compilation.
// This file is auto-generated. See "generate_kernels.py"
#include "flash_bwd_launch_template.h"
template<>
void run_mha_bwd_<cutlass::half_t, 96>(Flash_bwd_params &params, cudaStream_t stream) {
run_mha_bwd_hdim96<cutlass::half_t>(params, stream);
}
csrc/flash_attn/src/flash_bwd_kernel.h deleted 100644 → 0
View file @ 06c23892
/***************************************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <cute/algorithm/copy.hpp>
#include <cutlass/cutlass.h>
#include <cutlass/array.h>
#include <cutlass/numeric_types.h>
#include "block_info.h"
#include "kernel_traits.h"
#include "utils.h"
#include "softmax.h"
#include "mask.h"
#include "dropout.h"
#include "alibi.h"
namespace flash {
using namespace cute;
////////////////////////////////////////////////////////////////////////////////////////////////////
template <int MMA_N,
class... Args,
class TiledMMA>
CUTE_HOST_DEVICE
auto
make_tiled_copy_B_warpcontiguousN(Copy_Atom<Args...> const& copy_atom,
TiledMMA const& tiled_mma) {
constexpr int TileShape_N = decltype(tiled_mma.template tile_size_mnk<1>())::value;
constexpr int TileShape_K = decltype(tiled_mma.template tile_size_mnk<2>())::value;
using AtomShape_MNK = typename TiledMMA::AtomShape_MNK;
constexpr int AtomShape_N = decltype(size<1>(AtomShape_MNK{}))::value;
// Divide by 2 because right now we always use 2 for the ValLayout
constexpr int kNWarpsN = TileShape_N / AtomShape_N / 2;
constexpr int MMAStride_N = MMA_N * AtomShape_N * 2;
// This gives the correct layout, idk why.
// auto t = make_tile(Layout<Shape<Shape<_8, _2>, _2>,
// Stride<Stride<_1, _64>, _8> >{},
// auto t = make_tile(Layout<Shape<_8, _2, _2>,
// Stride<_1, _64, _8> >{},
auto t = make_tile(Layout<Shape<Int<AtomShape_N>, Int<kNWarpsN>, _2>, // (8, 2, 2) or (8, 4, 2)
Stride<_1, Int<MMAStride_N>, _8> >{}, // (1, 64, 8) or (1, 32, 8)
make_layout(Int<TileShape_K>{}));
// if (cute::thread0()) {printf("make_tiled_copy_B_warpcontiguousN "); print(t); printf("\n"); }
return make_tiled_copy_impl(copy_atom, tiled_mma.get_layoutB_TV(), t);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template <int MMA_N,
class... Args,
class TiledMMA>
CUTE_HOST_DEVICE
auto
make_tiled_copy_C_warpcontiguousN(Copy_Atom<Args...> const& copy_atom,
TiledMMA const& tiled_mma) {
constexpr int TileShape_M = decltype(tiled_mma.template tile_size_mnk<0>())::value;
constexpr int TileShape_N = decltype(tiled_mma.template tile_size_mnk<1>())::value;
using AtomShape_MNK = typename TiledMMA::AtomShape_MNK;
constexpr int AtomShape_N = decltype(size<1>(AtomShape_MNK{}))::value;
// Divide by 2 because right now we always use 2 for the ValLayout
constexpr int kNWarpsN = TileShape_N / AtomShape_N / 2;
constexpr int MMAStride_N = MMA_N * AtomShape_N * 2;
auto t = make_tile(make_layout(Int<TileShape_M>{}),
Layout<Shape<Int<AtomShape_N>, Int<kNWarpsN>, _2>, // (8, 2, 2) or (8, 4, 2)
Stride<_1, Int<MMAStride_N>, _8> >{}); // (1, 64, 8) or (1, 32, 8)
// if (cute::thread0()) {printf("make_tiled_copy_C_warpcontiguousN "); print(t); printf("\n"); }
return make_tiled_copy_impl(copy_atom, tiled_mma.get_layoutC_TV(), t);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Has_alibi, bool Is_even_MN, bool Is_even_K, bool Is_first, bool Is_last, bool Seq_parallel=false, typename Params>
inline __device__ void compute_dq_dk_dv_1colblock(const Params &params, const int bidb, const int bidh, const int n_block) {
using Element = typename Kernel_traits::Element;
using ElementAccum = typename Kernel_traits::ElementAccum;
using index_t = typename Kernel_traits::index_t;
// Shared memory.
extern __shared__ char smem_[];
// The thread index.
const int tidx = threadIdx.x;
constexpr int kBlockM = Kernel_traits::kBlockM;
constexpr int kBlockN = Kernel_traits::kBlockN;
constexpr int kHeadDim = Kernel_traits::kHeadDim;
constexpr int MMA_N_SdP = kBlockN / decltype(typename Kernel_traits::TiledMmaSdP{}.template tile_size_mnk<1>())::value;
constexpr int AtomLayoutMS = Kernel_traits::AtomLayoutMSdP;
constexpr bool Double_buffer = !Kernel_traits::No_double_buffer;
const BlockInfo</*Varlen=*/!Is_even_MN> binfo(params, bidb);
if (n_block * kBlockN >= binfo.actual_seqlen_k) return;
int m_block_max = cute::ceil_div(binfo.actual_seqlen_q, kBlockM);
if (Is_local) {
m_block_max = std::min(m_block_max, cute::ceil_div((n_block + 1) * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k + params.window_size_left, kBlockM));
}
const index_t row_offset_q = binfo.q_offset(params.q_batch_stride, params.q_row_stride, bidb)
+ (m_block_max - 1) * kBlockM * params.q_row_stride + bidh * params.q_head_stride;
const index_t row_offset_k = binfo.k_offset(params.k_batch_stride, params.k_row_stride, bidb)
+ n_block * kBlockN * params.k_row_stride + (bidh / params.h_h_k_ratio) * params.k_head_stride;
const index_t row_offset_v = binfo.k_offset(params.v_batch_stride, params.v_row_stride, bidb)
+ n_block * kBlockN * params.v_row_stride + (bidh / params.h_h_k_ratio) * params.v_head_stride;
const index_t row_offset_do = binfo.q_offset(params.do_batch_stride, params.do_row_stride, bidb)
+ (m_block_max - 1) * kBlockM * params.do_row_stride + bidh * params.do_head_stride;
const index_t row_offset_o = binfo.q_offset(params.o_batch_stride, params.o_row_stride, bidb)
+ (m_block_max - 1) * kBlockM * params.o_row_stride + bidh * params.o_head_stride;
const index_t row_offset_dq = binfo.q_offset(params.dq_batch_stride, params.dq_row_stride, bidb)
+ (m_block_max - 1) * kBlockM * params.dq_row_stride + bidh * params.dq_head_stride;
const index_t row_offset_dq_accum = binfo.q_offset(params.seqlen_q_rounded * params.h * params.d_rounded, params.h * params.d_rounded, bidb)
+ ((m_block_max - 1) * kBlockM + (params.cu_seqlens_q == nullptr ? 0 : 128 * bidb)) * params.h * params.d_rounded + bidh * params.d_rounded
// If deterministic, each thread block will do atomicAdd to a different dQ_accum buffer.
+ (!params.deterministic ? 0 : blockIdx.x * params.dq_accum_split_stride);
const index_t row_offset_lse = (bidb * params.h + bidh) * params.seqlen_q
+ (m_block_max - 1) * kBlockM;
const index_t row_offset_dpsum = (bidb * params.h + bidh) * params.seqlen_q_rounded
+ (m_block_max - 1) * kBlockM;
Tensor gQ = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.q_ptr) + row_offset_q),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.q_row_stride, _1{}));
Tensor gK = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.k_ptr) + row_offset_k),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.k_row_stride, _1{}));
Tensor gV = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.v_ptr) + row_offset_v),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.v_row_stride, _1{}));
Tensor gdO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.do_ptr) + row_offset_do),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.do_row_stride, _1{}));
Tensor gO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.o_ptr) + row_offset_o),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.o_row_stride, _1{}));
Tensor gdQ = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dq_ptr) + row_offset_dq),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.dq_row_stride, _1{}));
Tensor gdQaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dq_accum_ptr) + row_offset_dq_accum),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.h * params.d_rounded, _1{}));
Tensor gLSE = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.softmax_lse_ptr) + row_offset_lse),
Shape<Int<kBlockM>>{}, Stride<_1>{});
Tensor gdPsum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dsoftmax_sum) + row_offset_dpsum),
Shape<Int<kBlockM>>{}, Stride<_1>{});
Tensor sQ = make_tensor(make_smem_ptr(reinterpret_cast<Element *>(smem_)),
typename Kernel_traits::SmemLayoutQdO{});
Tensor sQt = make_tensor(sQ.data(), typename Kernel_traits::SmemLayoutQdOtransposed{});
Tensor sQtNoSwizzle = make_tensor(sQ.data(), typename Kernel_traits::SmemLayoutQdOtransposedNoSwizzle{});
// Double buffer for sQ
Tensor sdO = make_tensor(sQ.data() + (Double_buffer ? 2 : 1) * size(sQ), typename Kernel_traits::SmemLayoutQdO{});
Tensor sdOt = make_tensor(sdO.data(), typename Kernel_traits::SmemLayoutQdOtransposed{});
Tensor sdOtransposedNoSwizzle = make_tensor(sdO.data(),
typename Kernel_traits::SmemLayoutQdOtransposedNoSwizzle{});
Tensor sK = make_tensor(sdO.data() + size(sdO), typename Kernel_traits::SmemLayoutKV{});
Tensor sV = make_tensor(sK.data() + size(sK), typename Kernel_traits::SmemLayoutKV{});
Tensor sKt = make_tensor(sK.data(), typename Kernel_traits::SmemLayoutKtransposed{});
Tensor sKtNoSwizzle = make_tensor(sK.data(), typename Kernel_traits::SmemLayoutKtransposedNoSwizzle{});
Tensor sdS = make_tensor(!Kernel_traits::Is_V_in_regs ? sV.data() + size(sV) : sK.data() + size(sK),
typename Kernel_traits::SmemLayoutPdS{});
Tensor sdSt = make_tensor(sdS.data(), typename Kernel_traits::SmemLayoutPdStransposed{});
Tensor sdStNoSwizzle = make_tensor(sdS.data(), typename Kernel_traits::SmemLayoutPdStransposedNoSwizzle{});
Tensor sP = make_tensor(sdS.data() + size(sdS), typename Kernel_traits::SmemLayoutPdS{});
Tensor sPt = make_tensor(sP.data(), typename Kernel_traits::SmemLayoutPdStransposed{});
Tensor sPtNoSwizzle = make_tensor(sP.data(), typename Kernel_traits::SmemLayoutPdStransposedNoSwizzle{});
// sP and sdQ share the same memory so be careful
Tensor sdQ = make_tensor(sP.data(), typename Kernel_traits::SmemLayoutdQ{});
typename Kernel_traits::GmemTiledCopyQKV gmem_tiled_copy_QKV;
auto gmem_thr_copy_QKV = gmem_tiled_copy_QKV.get_thread_slice(tidx);
using GmemTiledCopydO = std::conditional_t<
Is_first,
typename Kernel_traits::GmemTiledCopydO,
typename Kernel_traits::GmemTiledCopyQKV
>;
GmemTiledCopydO gmem_tiled_copy_dO;
auto gmem_thr_copy_dO = gmem_tiled_copy_dO.get_thread_slice(tidx);
typename Kernel_traits::GmemTiledCopydQ gmem_tiled_copy_dQ;
auto gmem_thr_copy_dQ = gmem_tiled_copy_dQ.get_thread_slice(tidx);
using GmemLayoutAtomdQaccum = std::conditional_t<
!Seq_parallel,
typename Kernel_traits::GmemTiledCopydQaccum,
typename Kernel_traits::GmemTiledCopydQaccumAtomicAdd
>;
GmemLayoutAtomdQaccum gmem_tiled_copy_dQaccum;
auto gmem_thr_copy_dQaccum = gmem_tiled_copy_dQaccum.get_thread_slice(tidx);
Tensor tQgQ = gmem_thr_copy_QKV.partition_S(gQ);
Tensor tQsQ = gmem_thr_copy_QKV.partition_D(sQ);
Tensor tdOgdO = gmem_thr_copy_dO.partition_S(gdO);
Tensor tdOsdO = gmem_thr_copy_dO.partition_D(sdO);
Tensor tdOgO = gmem_thr_copy_dO.partition_S(gO);
Tensor tKgK = gmem_thr_copy_QKV.partition_S(gK); // (KCPY, KCPY_N, KCPY_K)
Tensor tKsK = gmem_thr_copy_QKV.partition_D(sK);
Tensor tVgV = gmem_thr_copy_QKV.partition_S(gV); // (VCPY, VCPY_N, VCPY_K)
Tensor tVsV = gmem_thr_copy_QKV.partition_D(sV);
Tensor tdQsdQ = gmem_thr_copy_dQ.partition_S(sdQ); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdQgdQ = gmem_thr_copy_dQ.partition_D(gdQ);
Tensor tdQgdQaccum = gmem_thr_copy_dQaccum.partition_D(gdQaccum);
// if (cute::thread0()) { print(tdQgdQaccum.layout()); printf("\n"); }
// __syncthreads();
// if (blockIdx.x == 0 && blockIdx.y == 0 && blockIdx.z == 0 && tidx < 64) {
// printf("tidx = %d, tdQgdQaccum = 0x%p\n", tidx, tdQgdQaccum.data());
// }
typename Kernel_traits::TiledMmaSdP tiled_mma_sdp;
auto thr_mma_sdp = tiled_mma_sdp.get_thread_slice(tidx);
Tensor tSrQ = thr_mma_sdp.partition_fragment_A(sQ); // (MMA,MMA_N,MMA_K)
Tensor tSrK = thr_mma_sdp.partition_fragment_B(sK); // (MMA,MMA_N,MMA_K)
Tensor tdPrdO = thr_mma_sdp.partition_fragment_A(sdO); // (MMA,MMA_N,MMA_K)
Tensor tdPrV = thr_mma_sdp.partition_fragment_B(sV); // (MMA,MMA_N,MMA_K)
typename Kernel_traits::TiledMmadKV tiled_mma_dkv;
auto thr_mma_dkv = tiled_mma_dkv.get_thread_slice(tidx);
Tensor tdKrdSt = thr_mma_dkv.partition_fragment_A(sdStNoSwizzle); // (MMA, MMA_N, MMA_N)
Tensor tdKrQt = thr_mma_dkv.partition_fragment_B(sQtNoSwizzle); // (MMA, MMA_K, MMA_N)
Tensor tdVrPt = thr_mma_dkv.partition_fragment_A(sPtNoSwizzle); // (MMA, MMA_N, MMA_N)
Tensor tdVrdO = thr_mma_dkv.partition_fragment_B(sdOtransposedNoSwizzle); // (MMA, MMA_K, MMA_N)
typename Kernel_traits::TiledMmadQ tiled_mma_dq;
auto thr_mma_dq = tiled_mma_dq.get_thread_slice(tidx);
Tensor tdQrdS = thr_mma_dq.partition_fragment_A(sdS); // (MMA, MMA_N, MMA_N)
Tensor tdQrKt = thr_mma_dq.partition_fragment_B(sKtNoSwizzle); // (MMA, MMA_K, MMA_N)
Tensor acc_dk = partition_fragment_C(tiled_mma_dkv, Shape<Int<kBlockN>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
Tensor acc_dv = partition_fragment_C(tiled_mma_dkv, Shape<Int<kBlockN>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
//
// Copy Atom retiling
//
auto smem_tiled_copy_QdO = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_sdp);
auto smem_thr_copy_QdO = smem_tiled_copy_QdO.get_thread_slice(tidx);
Tensor tSsQ = smem_thr_copy_QdO.partition_S(sQ);
Tensor tdPsdO = smem_thr_copy_QdO.partition_S(sdO);
// auto smem_thr_copy_KV = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_sdp).get_thread_slice(tidx);
auto smem_tiled_copy_KV = make_tiled_copy_B_warpcontiguousN<MMA_N_SdP>(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_sdp);
auto smem_thr_copy_KV = smem_tiled_copy_KV.get_thread_slice(tidx);
Tensor tSsK = smem_thr_copy_KV.partition_S(sK);
// if (cute::thread(0, 0) && n_block == 0) { printf("sK layout: "); print(sK.layout()); printf("\n"); }
// if (cute::thread(0, 0) && n_block == 0) { print(tSsK.layout()); printf("\n"); }
Tensor tdPsV = smem_thr_copy_KV.partition_S(sV);
// Partition sP and sdS to match the accumulator partitioning
// This has to be tiled_mma_sdp, not tiled_mma_dkv
// auto smem_thr_copy_PdS = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomPdS{}, tiled_mma_sdp).get_thread_slice(tidx);
auto smem_tiled_copy_PdS = make_tiled_copy_C_warpcontiguousN<MMA_N_SdP>(typename Kernel_traits::SmemCopyAtomPdS{}, tiled_mma_sdp);
auto smem_thr_copy_PdS = smem_tiled_copy_PdS.get_thread_slice(tidx);
Tensor tPsP = smem_thr_copy_PdS.partition_D(sP); // ((Atom,AtomNum),PIPE_M,PIPE_N)
// if (cute::thread(0, 0) && n_block == 0) { printf("sP layout: "); print(sP.layout()); printf("\n"); }
// if (cute::thread(0, 0) && n_block == 0) { print(tPsP.layout()); printf("\n"); }
// if (n_block == 0 && blockIdx.x == 0 && blockIdx.y == 0 && tidx < 64) {
// printf("tidx=%d, tPsP = 0x%p\n", tidx, tPsP.data());
// }
Tensor tdSsdS = smem_thr_copy_PdS.partition_D(sdS); // ((Atom,AtomNum),PIPE_M,PIPE_N)
auto smem_tiled_copy_PdSt = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma_dkv);
auto smem_thr_copy_PdSt = smem_tiled_copy_PdSt.get_thread_slice(tidx);
Tensor tdVsPt = smem_thr_copy_PdSt.partition_S(sPt);
Tensor tdKsdSt = smem_thr_copy_PdSt.partition_S(sdSt);
auto smem_tiled_copy_QdOt = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma_dkv);
auto smem_thr_copy_QdOt = smem_tiled_copy_QdOt.get_thread_slice(tidx);
Tensor tdVsdOt = smem_thr_copy_QdOt.partition_S(sdOt);
Tensor tdKsQt = smem_thr_copy_QdOt.partition_S(sQt);
auto smem_tiled_copy_dS = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtom{}, tiled_mma_dq);
auto smem_thr_copy_dS = smem_tiled_copy_dS.get_thread_slice(tidx);
Tensor tdQsdS = smem_thr_copy_dS.partition_S(sdS);
auto smem_tiled_copy_Kt = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma_dq);
auto smem_thr_copy_Kt = smem_tiled_copy_Kt.get_thread_slice(tidx);
Tensor tdQsKt = smem_thr_copy_Kt.partition_S(sKt);
auto smem_tiled_copy_dQ = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomdQ{}, tiled_mma_dq);
auto smem_thr_copy_dQ = smem_tiled_copy_dQ.get_thread_slice(tidx);
Tensor taccdQsdQ = smem_thr_copy_dQ.partition_D(sdQ); // ((Atom,AtomNum),PIPE_M,PIPE_N)
//
// PREDICATES
//
Tensor cQ = make_identity_tensor(make_shape(size<0>(sQ), size<1>(sQ))); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor cKV = make_identity_tensor(make_shape(size<0>(sK), size<1>(sK))); // (BLK_N,BLK_K) -> (blk_n,blk_k)
Tensor tQcQ = gmem_thr_copy_QKV.partition_D(cQ);
Tensor tKVcKV = gmem_thr_copy_QKV.partition_D(cKV);
// Allocate predicate tensors for k
Tensor tQpQ = make_tensor<bool>(make_shape(size<2>(tQsQ)));
Tensor tKVpKV = make_tensor<bool>(make_shape(size<2>(tKsK)));
// Set predicates for k bounds
if (!Is_even_K) {
#pragma unroll
for (int k = 0; k < size(tQpQ); ++k) { tQpQ(k) = get<1>(tQcQ(0, 0, k)) < params.d; }
#pragma unroll
for (int k = 0; k < size(tKVpKV); ++k) { tKVpKV(k) = get<1>(tKVcKV(0, 0, k)) < params.d; }
}
// Prologue
// We'll advance gdQ and gdQaccum before the 1st read/write.
tdQgdQ.data() = tdQgdQ.data() + kBlockM * params.dq_row_stride;
tdQgdQaccum.data() = tdQgdQaccum.data() + kBlockM * params.h * params.d_rounded;
int m_block = m_block_max - 1;
int m_block_min = (!Is_causal && !Is_local)
? 0
: std::max(0, (n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k - params.window_size_right) / kBlockM);
// If not local, we're guaranteed that m_block_min <= m_block:
// We checked earlier that n_block * kBlockN < actual_seqlen_k, so in the causal case,
// n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k < actual_seqlen_q.
// So m_block_min <= (actual_seqlen_q - 1) / kBlockM.
// Recall that m_block_max = cute::ceil_div(binfo.actual_seqlen_q, kBlockM) = (actual_seqlen_q + kBlockM - 1) / kBlockM.
// So m_block_m - 1 = (actual_seqlen_q - 1) / kBlockM.
// We conclude that m_block_min <= m_block, so we will always have at least 1 iteration of the for loop.
// However, if local, then this possible to have some blocks of K & V not attending to any query.
// We might need to exit early and write 0 to dK and dV for those blocks.
// Otherwise we get wrong result for the case where we don't enter the for loop.
// And we might read OOB elements from gQ and gdO.
// This also covers the case where actual_seqlen_q == 0
if ((Is_local || !Is_even_MN) && m_block < m_block_min) {
const index_t row_offset_dk = binfo.k_offset(params.dk_batch_stride, params.dk_row_stride, bidb)
+ n_block * kBlockN * params.dk_row_stride + bidh * params.dk_head_stride;
const index_t row_offset_dv = binfo.k_offset(params.dv_batch_stride, params.dv_row_stride, bidb)
+ n_block * kBlockN * params.dv_row_stride + bidh * params.dv_head_stride;
Tensor gdK = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dk_ptr) + row_offset_dk),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.dk_row_stride, _1{}));
Tensor gdV = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dv_ptr) + row_offset_dv),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.dv_row_stride, _1{}));
typename Kernel_traits::GmemTiledCopydKV gmem_tiled_copy_dKV;
auto gmem_thr_copy_dKV = gmem_tiled_copy_dKV.get_thread_slice(tidx);
Tensor tdKgdK = gmem_thr_copy_dKV.partition_D(gdK);
Tensor tdVgdV = gmem_thr_copy_dKV.partition_D(gdV);
Tensor tdKrdK = make_tensor<Element>(shape(tdKgdK));
Tensor tdVrdV = make_tensor<Element>(shape(tdVgdV));
clear(tdKrdK);
clear(tdVrdV);
Tensor cdKV = make_identity_tensor(make_shape(size<0>(gdK), size<1>(gdK))); // (BLK_N,BLK_K) -> (blk_n,blk_k)
Tensor tdKVcdKV = gmem_thr_copy_dKV.partition_D(cdKV);
Tensor tdKVpdKV = make_tensor<bool>(make_shape(size<2>(tdKgdK)));
#pragma unroll
for (int k = 0; k < size(tdKVpdKV); ++k) { tdKVpdKV(k) = get<1>(tdKVcdKV(0, 0, k)) < params.d; }
// Clear_OOB_K must be false since we don't want to write zeros to gmem
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKrdK, tdKgdK, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
);
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdVrdV, tdVgdV, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
);
return;
}
if (Double_buffer && m_block % 2 == 1) { // Double buffer for sQ
tQsQ.data() = tQsQ.data() + size(sQ);
tSsQ.data() = tSsQ.data() + size(sQ);
tdKsQt.data() = tdKsQt.data() + size(sQ);
}
if ((!Is_first && !Seq_parallel) || params.deterministic) { __syncthreads(); }
if (Kernel_traits::Is_V_in_regs) {
// Clear the smem tiles to account for predicated off loads
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV, binfo.actual_seqlen_k - n_block * kBlockN
);
flash::cp_async_fence();
}
Tensor tdOrdO = make_fragment_like(tdOgdO);
Tensor tdOrO = make_fragment_like(tdOgO);
if (!Is_first) {
// Clear the smem tiles to account for predicated off loads
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgdO, tdOsdO, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
} else {
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgdO, tdOrdO, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgO, tdOrO, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
}
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tQgQ, tQsQ, tQcQ, tQpQ, binfo.actual_seqlen_q - m_block * kBlockM
);
Tensor caccS = make_identity_tensor(Shape<Int<kBlockM>, Int<kBlockN>>{}); // (BLK_M,BLK_N) -> (blk_m,blk_n)
Tensor taccScS = thr_mma_sdp.partition_C(caccS); // (MMA,MMA_N,MMA_N)
static_assert(decltype(size<0>(taccScS))::value == 4);
// Convert to ((2, 2), MMA_N, MMA_N) then take only the row indices.
Tensor taccScS_row = logical_divide(taccScS, Shape<_2>{})(make_coord(0, _), _, 0);
Tensor lse = make_tensor<ElementAccum>(Shape<Int<decltype(size(taccScS_row))::value>>{});
#pragma unroll
for (int mi = 0; mi < size(lse); ++mi) {
const int row = get<0>(taccScS_row(mi));
lse(mi) = Is_even_MN || row < binfo.actual_seqlen_q - m_block * kBlockM ? gLSE(row) : INFINITY;
}
// We want LSE = inf if the row is OOB. In that case Q would be zero, K would be zero,
// and scores would be zero. With LSE = 0, probs will be all 1's, and when we multiply
// with V (which would be zero), we're fine. However, with ALiBi, we might modify these
// scores, and probs can become NaN. Instead if we set LSE = inf for OOB rows, probs are always 0.
// Tensor tKrK = make_fragment_like(tKsK);
// // cute::copy(gmem_tiled_copy_QKV, tKgK(_, _, _, 0), tKrK);
// cute::copy(gmem_tiled_copy_QKV, tKgK, tKrK);
// // if (cute::thread(1, 0)) { print(tKrK); }
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV, binfo.actual_seqlen_k - n_block * kBlockN
);
if (!Kernel_traits::Is_V_in_regs) {
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV, binfo.actual_seqlen_k - n_block * kBlockN
);
}
flash::cp_async_fence();
// if (cute::thread0()) { print(tdOgdO.layout()); printf("\n"); print(tdOrdO); print(tdOrO); }
if (Is_first) {
cute::copy(tdOrdO, tdOsdO);
dot_do_o<Kernel_traits::kGmemThreadsPerRow>(tdOrdO, tdOrO, gdPsum,
Kernel_traits::kNThreads / (Kernel_traits::kGmemThreadsPerRow), params.p_dropout);
}
if (Kernel_traits::Is_V_in_regs) {
cute::cp_async_wait<1>();
__syncthreads();
Tensor tdPrV_copy_view = smem_thr_copy_KV.retile_D(tdPrV);
CUTE_STATIC_ASSERT_V(size<1>(tdPsV) == size<1>(tdPrV_copy_view)); // M
cute::copy(smem_tiled_copy_KV, tdPsV, tdPrV_copy_view);
}
flash::Dropout dropout(params.rng_state[0], params.rng_state[1], params.p_dropout_in_uint8_t,
bidb, bidh, tidx, params.h);
clear(acc_dv);
clear(acc_dk);
const float alibi_slope = !Has_alibi || params.alibi_slopes_ptr == nullptr ? 0.0f : reinterpret_cast<float *>(params.alibi_slopes_ptr)[bidb * params.alibi_slopes_batch_stride + bidh] / params.scale_softmax;
flash::Alibi<Is_causal> alibi(alibi_slope, binfo.actual_seqlen_k, binfo.actual_seqlen_q);
for (; m_block >= m_block_min; --m_block) {
Tensor acc_s = partition_fragment_C(tiled_mma_sdp, Shape<Int<kBlockM>, Int<kBlockN>>{}); // (MMA=4, MMA_N, MMA_N)
clear(acc_s);
cute::cp_async_wait<0>();
__syncthreads();
Tensor dP_sum = make_fragment_like(lse);
#pragma unroll
for (int mi = 0; mi < size(lse); ++mi) { dP_sum(mi) = gdPsum(get<0>(taccScS_row(mi))); }
// if (cute::thread0()) { print(sK); }
// Tensor tSrK_copy_view = smem_thr_copy_KV.retile_D(tSrK);
// #pragma unroll
// for (int k = 0; k < size<2>(tSrK_copy_view); ++k) {
// cute::copy(smem_tiled_copy_KV, tSsK(_, _, k), tSrK_copy_view(_, _, k));
// }
// if (cute::thread0()) { print(tSrK); }
flash::gemm(acc_s, tSrQ, tSrK, tSsQ, tSsK, tiled_mma_sdp,
smem_tiled_copy_QdO, smem_tiled_copy_KV, smem_thr_copy_QdO, smem_thr_copy_KV);
// Reshape acc_s from (MMA=4, MMA_N, MMA_N) to (col=(2, MMA_N), row=(2, MMA_N))
Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
// if (cute::thread(32, 0)) { print(scores); }
if (Has_alibi) {
alibi.apply_alibi(scores, n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
m_block * kBlockM + get<0>(taccScS_row(0)), AtomLayoutMS * 16);
}
// TD [2023-07-29]: I was thinking that we don't need to mask out the elements beyond
// actual_seqlen_k, because acc_s would be some finite value for those indices.
// In the end when we multiply with K to get dQ, the corresponding values of K would be 0,
// so the result would still be correct.
// However, it's possible that the values in acc_s are so large that they overflow
// when we multiply with dP and convert to fp16, resulting in Inf in dS and NaNs in dQ.
// So we need to mask out the elements beyond actual_seqlen_k.
if (!Is_causal && !Is_local) {
if (!Is_even_MN && (n_block + 1) * kBlockN >= binfo.actual_seqlen_k) {
flash::apply_mask(scores, binfo.actual_seqlen_k,
n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16);
}
} else if (Is_causal) {
// Putting this causal masking right after acc_s is *much* slower for some reason.
// TD [2023-08-16]: We need the 2nd condition because if seqlen_q is long and seqlen_k is short
// (e.g., 256 and 2), the 2nd block of seqlen_q (from 128 to 255), we're not doing causal masking.
// But we still want to mask out elements beyond actual_seqlen_k.
if (m_block * kBlockM < (n_block + 1) * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k
|| (!Is_even_MN && (n_block + 1) * kBlockN >= binfo.actual_seqlen_k)) {
flash::apply_mask_causal(scores, n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
binfo.actual_seqlen_k, m_block * kBlockM + get<0>(taccScS_row(0)),
binfo.actual_seqlen_q,
// binfo.actual_seqlen_k, m_block * kBlockM + (tidx / 32) % AtomLayoutMS * 16 + (tidx % 32) / 4,
AtomLayoutMS * 16);
}
} else if (Is_local) {
if (m_block * kBlockM < (n_block + 1) * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k - params.window_size_right
|| (m_block + 1) * kBlockM >= n_block * kBlockN + binfo.actual_seqlen_q - binfo.actual_seqlen_k + params.window_size_left
|| (!Is_even_MN && (n_block + 1) * kBlockN >= binfo.actual_seqlen_k)) {
flash::apply_mask_local(scores, n_block * kBlockN + (tidx / 32 / AtomLayoutMS) * MMA_N_SdP * 16,
binfo.actual_seqlen_k, m_block * kBlockM + get<0>(taccScS_row(0)),
binfo.actual_seqlen_q, AtomLayoutMS * 16,
params.window_size_left, params.window_size_right);
}
}
// if (cute::thread(32, 0)) { print(scores); }
// Compute the exponential value.
flash::scale_apply_exp2</*scale_max=*/false>(scores, lse, params.scale_softmax_log2);
if constexpr (Is_dropout) {
int warp_id = tidx / 32;
int block_row_idx = m_block * (kBlockM / 16) + warp_id % AtomLayoutMS;
// Need col to be multiples of 32, since we're doing dropout with block of 16 x 32
static_assert(MMA_N_SdP % 2 == 0);
int block_col_idx = n_block * (kBlockN / 32) + (warp_id / AtomLayoutMS) * (MMA_N_SdP / 2);
dropout.template apply_dropout</*encode_dropout_in_sign_bit=*/true>(
acc_s, block_row_idx, block_col_idx, AtomLayoutMS
);
}
// Convert scores from fp32 to fp16/bf16
Tensor rP = !Is_dropout
? flash::convert_type<Element>(acc_s)
: flash::convert_type_relu<Element>(acc_s);
// Reshape rP from (MMA=4, MMA_M, MMA_N) to ((4, 2), MMA_N, MMA_N / 2)
// if using m16n8k16 or (4, MMA_N, MMA_N) if using m16n8k8.
Tensor tPrP = make_tensor(rP.data(), flash::convert_layout_acc_Aregs<Kernel_traits::TiledMmaSdP>(rP.layout()));
Tensor tPaP = smem_thr_copy_PdS.retile_S(tPrP); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_PdS, tPaP, tPsP);
// if (cute::thread0()) { print(tPaP); }
// __syncthreads();
// if (cute::thread0()) { print(sP); }
Tensor acc_dp = partition_fragment_C(tiled_mma_sdp, Shape<Int<kBlockM>, Int<kBlockN>>{}); // (MMA=4, MMA_N, MMA_N)
CUTE_STATIC_ASSERT_V(size<0>(acc_dp) == size<0>(acc_s)); // MMA
CUTE_STATIC_ASSERT_V(size<1>(acc_dp) == size<1>(acc_s)); // MMA
CUTE_STATIC_ASSERT_V(size<2>(acc_dp) == size<2>(acc_s)); // MMA
clear(acc_dp);
// Tensor acc_dp_reshaped = make_tensor(acc_dp.data(), flash::convert_layout_acc_rowcol(acc_dp.layout()));
// #pragma unroll
// for (int mi = 0; mi < size<0>(acc_dp_reshaped); ++mi) {
// #pragma unroll
// for (int ni = 0; ni < size<1>(acc_dp_reshaped); ++ni) {
// acc_dp_reshaped(mi, ni) = -dP_sum(mi);
// }
// }
// if (cute::thread0()) { print(dP_sum); }
flash::gemm</*A_in_regs=*/false, /*B_in_regs=*/Kernel_traits::Is_V_in_regs>(
acc_dp, tdPrdO, tdPrV, tdPsdO, tdPsV, tiled_mma_sdp,
smem_tiled_copy_QdO, smem_tiled_copy_KV, smem_thr_copy_QdO, smem_thr_copy_KV
);
// Reshape acc_dp from (MMA=4, MMA_N, MMA_N) to (col=(2, MMA_N), row=(2, MMA_N))
Tensor dS = make_tensor(acc_dp.data(), scores.layout());
auto pointwise_mult = [](float p, float dp, float d) {
return p * (!Is_dropout || p >= 0 ? dp - d : d);
};
#pragma unroll
for (int mi = 0; mi < size<0>(dS); ++mi) {
#pragma unroll
for (int ni = 0; ni < size<1>(dS); ++ni) {
dS(mi, ni) = pointwise_mult(scores(mi, ni), dS(mi, ni), dP_sum(mi));
}
}
// if (cute::thread0()) { print(dS); }
Tensor acc_dq = partition_fragment_C(tiled_mma_dq, Shape<Int<kBlockM>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
tdQgdQaccum.data() = tdQgdQaccum.data() + (-int(kBlockM * params.h * params.d_rounded));
if (Is_first || Seq_parallel) {
clear(acc_dq);
} else {
// Reshape acc_dq from (4, 1, 2) to (4, 2, 1) to write to gdQaccum
Tensor acc_dq_reshaped = make_tensor(acc_dq.data(),
make_layout(get<0>(acc_dq.layout()),
get<2>(acc_dq.layout()),
get<1>(acc_dq.layout())));
cute::copy(gmem_tiled_copy_dQaccum, tdQgdQaccum, acc_dq_reshaped);
}
if (Double_buffer && m_block > m_block_min) {
// Double buffer for sQ
const int sQ_offset = m_block % 2 == 0 ? size(sQ) : -size(sQ);
tQsQ.data() = tQsQ.data() + sQ_offset;
tSsQ.data() = tSsQ.data() + sQ_offset;
// Advance gQ
tQgQ.data() = tQgQ.data() + (-int(kBlockM * params.q_row_stride));
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tQgQ, tQsQ, tQcQ, tQpQ);
flash::cp_async_fence();
}
Tensor dS_reshaped = make_tensor(dS.data(), acc_dp.layout());
// Convert dS from fp32 to fp16
Tensor tdSrdS = flash::convert_type<Element>(dS_reshaped);
// if (cute::thread0()) { print(tPrP); }
Tensor tdSadS = smem_thr_copy_PdS.retile_S(tdSrdS); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_PdS, tdSadS, tdSsdS);
__syncthreads();
// Layout p_l = tPrP.layout();
// Tensor tdVrPt = make_tensor(tPrP.data(), make_layout(get<0>(p_l), get<2>(p_l), get<1>(p_l)));
// flash::gemm_rs(acc_dv, tdVrPt, tdVrdO, tdVsdOt, tiled_mma_dkv, smem_thr_copy_QdOt);
// Tensor tdKrdSt = make_tensor(tdSrdS.data(), tdVrPt.layout());
// flash::gemm_rs(acc_dk, tdKrdSt, tdKrQt, tdKsQt, tiled_mma_dkv, smem_thr_copy_QdOt);
flash::gemm(acc_dv, tdVrPt, tdVrdO, tdVsPt, tdVsdOt, tiled_mma_dkv,
smem_tiled_copy_PdSt, smem_tiled_copy_QdOt, smem_thr_copy_PdSt, smem_thr_copy_QdOt);
// if (cute::thread0() && n_block == 0 && m_block == 0) { print(tdVrPt); }
// if (cute::thread0()) { print(acc_dv); }
__syncthreads(); // Need syncthreads since we're writing to the same sdO location
if (m_block > m_block_min) {
// Advance gdO
tdOgdO.data() = tdOgdO.data() + (-int(kBlockM * params.do_row_stride));
if (Is_first) {
tdOgO.data() = tdOgO.data() + (-int(kBlockM * params.o_row_stride));
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_dO, tdOgdO, tdOrdO, tQcQ, tQpQ);
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_dO, tdOgO, tdOrO, tQcQ, tQpQ);
} else {
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_dO, tdOgdO, tdOsdO, tQcQ, tQpQ);
flash::cp_async_fence();
}
}
flash::gemm(acc_dq, tdQrdS, tdQrKt, tdQsdS, tdQsKt, tiled_mma_dq,
smem_tiled_copy_dS, smem_tiled_copy_Kt, smem_thr_copy_dS, smem_thr_copy_Kt);
// if (cute::thread0()) { print(acc_dq); }
if (m_block > m_block_min) {
gLSE.data() = gLSE.data() + (-int(kBlockM));
#pragma unroll
for (int mi = 0; mi < size(lse); ++mi) { lse(mi) = gLSE(get<0>(taccScS_row(mi))); }
gdPsum.data() = gdPsum.data() + (-int(kBlockM));
}
if (!Is_last) {
// Reshape acc_dq from (4, 1, 2) to (4, 2, 1) to write to gdQaccum
Tensor acc_dq_reshaped = make_tensor(acc_dq.data(),
make_layout(get<0>(acc_dq.layout()),
get<2>(acc_dq.layout()),
get<1>(acc_dq.layout())));
if (!Seq_parallel) {
cute::copy(gmem_tiled_copy_dQaccum, acc_dq_reshaped, tdQgdQaccum);
} else {
// if (cute::thread0()) { print(acc_dq.layout()); printf("\n"); print(acc_dq_reshaped.layout()); printf("\n"); print(tdQgdQaccum.layout()); printf("\n"); }
CUTE_STATIC_ASSERT_V(size(acc_dq) == size(tdQgdQaccum));
#pragma unroll
for (int i = 0; i < size(acc_dq); ++i) { atomicAdd(&tdQgdQaccum(i), acc_dq(i)); }
}
} else {
#pragma unroll
for (int i = 0; i < size(acc_dq); ++i) { acc_dq(i) *= params.scale_softmax_rp_dropout; }
// Convert acc_dq from fp32 to fp16
Tensor rdQ = flash::convert_type<Element>(acc_dq);
Tensor taccdQrdQ = smem_thr_copy_dQ.retile_S(rdQ); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_dQ, taccdQrdQ, taccdQsdQ);
}
flash::gemm(acc_dk, tdKrdSt, tdKrQt, tdKsdSt, tdKsQt, tiled_mma_dkv,
smem_tiled_copy_PdSt, smem_tiled_copy_QdOt, smem_thr_copy_PdSt, smem_thr_copy_QdOt);
// if (cute::thread0()) { print(acc_dk); }
if (Double_buffer) { // Double buffer for sQ
tdKsQt.data() = tdKsQt.data() + (m_block % 2 == 0 ? size(sQ) : -size(sQ));
}
if (!Double_buffer && m_block > m_block_min) {
__syncthreads();
// Advance gQ
tQgQ.data() = tQgQ.data() + (-int(kBlockM * params.q_row_stride));
flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tQgQ, tQsQ, tQcQ, tQpQ);
flash::cp_async_fence();
}
if (Is_first && m_block > m_block_min) {
cute::copy(tdOrdO, tdOsdO);
dot_do_o<Kernel_traits::kGmemThreadsPerRow>(tdOrdO, tdOrO, gdPsum,
Kernel_traits::kNThreads / (Kernel_traits::kGmemThreadsPerRow), params.p_dropout);
}
if (Is_last) {
__syncthreads();
Tensor tdQrdQ = make_tensor<Element>(shape(tdQgdQ));
cute::copy(gmem_tiled_copy_dQ, tdQsdQ, tdQrdQ);
tdQgdQ.data() = tdQgdQ.data() + (-int(kBlockM * params.dq_row_stride));
Tensor cdQ = make_identity_tensor(Shape<Int<kBlockM>, Int<kHeadDim>>{}); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor tdQcdQ = gmem_thr_copy_dQ.partition_D(cdQ);
#pragma unroll
for (int m = 0; m < size<1>(tdQgdQ); ++m) {
if (Is_even_MN || get<0>(tdQcdQ(0, m, 0)) < binfo.actual_seqlen_q - m_block * kBlockM) {
cute::copy(gmem_tiled_copy_dQ, tdQrdQ(_, m, _), tdQgdQ(_, m, _));
}
}
}
}
// Epilogue
if (Is_dropout) {
#pragma unroll
for (int i = 0; i < size(acc_dv); ++i) { acc_dv(i) *= params.rp_dropout; }
}
#pragma unroll
for (int i = 0; i < size(acc_dk); ++i) { acc_dk(i) *= params.scale_softmax_rp_dropout; }
// Convert acc_dv from fp32 to fp16
Tensor rdK = flash::convert_type<Element>(acc_dk);
Tensor rdV = flash::convert_type<Element>(acc_dv);
Tensor sdK = make_tensor(sK.data(), typename Kernel_traits::SmemLayoutdKV{}); // (SMEM_N, SMEM_K)
Tensor sdV = make_tensor(sdK.data() + size(sdK), typename Kernel_traits::SmemLayoutdKV{}); // (SMEM_N, SMEM_K)
// Partition sdV and sdK to match the accumulator partitioning
auto smem_tiled_copy_dKV = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomdKV{}, tiled_mma_dkv);
auto smem_thr_copy_dKV = smem_tiled_copy_dKV.get_thread_slice(tidx);
Tensor taccdKrdK = smem_thr_copy_dKV.retile_S(rdK); // ((Atom,AtomNum), MMA_N, MMA_N)
Tensor taccdKsdK = smem_thr_copy_dKV.partition_D(sdK); // ((Atom,AtomNum),PIPE_M,PIPE_N)
Tensor taccdVrdV = smem_thr_copy_dKV.retile_S(rdV); // ((Atom,AtomNum), MMA_N, MMA_N)
Tensor taccdVsdV = smem_thr_copy_dKV.partition_D(sdV); // ((Atom,AtomNum),PIPE_M,PIPE_N)
// We need syncthreads here since we're writing to the same location as sK and sV.
// Without syncthreads, some thread might modify the location of sK while another thread
// is reading it for dQ gemm, leading to a race condition.
// If Is_last, there's already a __syncthreads() at the end of the loop.
if (!Is_last) { __syncthreads(); }
cute::copy(smem_tiled_copy_dKV, taccdKrdK, taccdKsdK);
cute::copy(smem_tiled_copy_dKV, taccdVrdV, taccdVsdV);
const index_t row_offset_dk = binfo.k_offset(params.dk_batch_stride, params.dk_row_stride, bidb)
+ n_block * kBlockN * params.dk_row_stride + bidh * params.dk_head_stride;
const index_t row_offset_dv = binfo.k_offset(params.dv_batch_stride, params.dv_row_stride, bidb)
+ n_block * kBlockN * params.dv_row_stride + bidh * params.dv_head_stride;
Tensor gdK = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dk_ptr) + row_offset_dk),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.dk_row_stride, _1{}));
Tensor gdV = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dv_ptr) + row_offset_dv),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.dv_row_stride, _1{}));
typename Kernel_traits::GmemTiledCopydKV gmem_tiled_copy_dKV;
auto gmem_thr_copy_dKV = gmem_tiled_copy_dKV.get_thread_slice(tidx);
Tensor tdKsdK = gmem_thr_copy_dKV.partition_S(sdK); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdKgdK = gmem_thr_copy_dKV.partition_D(gdK);
Tensor tdVsdV = gmem_thr_copy_dKV.partition_S(sdV); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdVgdV = gmem_thr_copy_dKV.partition_D(gdV);
__syncthreads();
Tensor tdKrdK = make_tensor<Element>(shape(tdKgdK));
cute::copy(gmem_tiled_copy_dKV, tdKsdK, tdKrdK);
Tensor tdVrdV = make_tensor<Element>(shape(tdVgdV));
cute::copy(gmem_tiled_copy_dKV, tdVsdV, tdVrdV);
Tensor cdKV = make_identity_tensor(make_shape(size<0>(sdK), size<1>(sdK))); // (BLK_N,BLK_K) -> (blk_n,blk_k)
Tensor tdKVcdKV = gmem_thr_copy_dKV.partition_D(cdKV);
Tensor tdKVpdKV = make_tensor<bool>(make_shape(size<2>(tdKgdK)));
#pragma unroll
for (int k = 0; k < size(tdKVpdKV); ++k) { tdKVpdKV(k) = get<1>(tdKVcdKV(0, 0, k)) < params.d; }
// Clear_OOB_K must be false since we don't want to write zeros to gmem
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKrdK, tdKgdK, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
);
flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdVrdV, tdVgdV, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_M, bool Is_even_K, typename Params>
inline __device__ void compute_dq_dk_dv(const Params &params) {
// The block index for the batch.
const int bidb = blockIdx.x;
// const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.y;
// const int bidh = blockIdx.z;
// The thread index.
const int tidx = threadIdx.x;
const int n_block_max = (params.seqlen_k + Kernel_traits::kBlockN - 1) / Kernel_traits::kBlockN;
if (n_block_max == 1) {
compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_M, Is_even_K, true, true>(params, bidb, bidh, 0);
} else {
// Iterating backward from n_block_max - 1 to 0 might save 1 register
compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_M, Is_even_K, true, false>(params, bidb, bidh, n_block_max - 1);
for (int n_block = n_block_max - 2; n_block > 0; n_block--) {
compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_M, Is_even_K, false, false>(params, bidb, bidh, n_block);
}
compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_M, Is_even_K, false, true>(params, bidb, bidh, 0);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_local, bool Has_alibi, bool Is_even_MN, bool Is_even_K, typename Params>
inline __device__ void compute_dq_dk_dv_seqk_parallel(const Params &params) {
// The block index for the batch.
const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.z;
// If deterministic, each thread block will do atomicAdd to a different dQ_accum buffer.
for (int n_block = blockIdx.x; n_block < (params.seqlen_k + Kernel_traits::kBlockN - 1) / Kernel_traits::kBlockN; n_block += gridDim.x) {
compute_dq_dk_dv_1colblock<Kernel_traits, Is_dropout, Is_causal, Is_local, Has_alibi, Is_even_MN, Is_even_K, false, false, /*Seq_parallel=*/true>(params, bidb, bidh, n_block);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace flash
csrc/flash_attn/src/flash_bwd_launch_template.h deleted 100644 → 0
View file @ 06c23892
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <ATen/cuda/CUDAContext.h>
#include "static_switch.h"
#include "flash.h"
#include "flash_bwd_preprocess_kernel.h"
#include "flash_bwd_kernel.h"
// Determine if the architecture supports FLASH and define a macro to handle parameter modifiers
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
#define ARCH_SUPPORTS_FLASH
#define KERNEL_PARAM_MODIFIER __grid_constant__
#else
#define KERNEL_PARAM_MODIFIER
#endif
// Define a macro for unsupported architecture handling to centralize the error message
#define FLASH_UNSUPPORTED_ARCH printf("FATAL: FlashAttention requires building with sm version sm80-sm90, but was built for < 8.0!");
// Use a macro to clean up kernel definitions
#define DEFINE_FLASH_BACKWARD_KERNEL(kernelName, ...) \
template<typename Kernel_traits, __VA_ARGS__> \
__global__ void kernelName(KERNEL_PARAM_MODIFIER const Flash_bwd_params params)
DEFINE_FLASH_BACKWARD_KERNEL(flash_bwd_dq_dk_dv_loop_kernel, bool Is_dropout, bool Is_causal, bool Has_alibi, bool Is_even_M, bool Is_even_K) {
#if defined(ARCH_SUPPORTS_FLASH)
flash::compute_dq_dk_dv<Kernel_traits, Is_dropout, Is_causal, Has_alibi, Is_even_M, Is_even_K>(params);
#else
FLASH_UNSUPPORTED_ARCH
#endif
}
DEFINE_FLASH_BACKWARD_KERNEL(flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel, bool Is_dropout, bool Is_causal, bool Is_local, bool Has_alibi, bool Is_even_MN, bool Is_even_K) {
#if defined(ARCH_SUPPORTS_FLASH)
static_assert(!(Is_causal && Is_local)); // If Is_local is true, Is_causal should be false
flash::compute_dq_dk_dv_seqk_parallel<Kernel_traits, Is_dropout, Is_causal, Is_local, Has_alibi, Is_even_MN, Is_even_K>(params);
#else
FLASH_UNSUPPORTED_ARCH
#endif
}
template<bool Clear_dQaccum=true, typename Kernel_traits>
__global__ void flash_bwd_dot_do_o_kernel(const Flash_bwd_params params) {
flash::compute_dot_do_o<Clear_dQaccum, Kernel_traits>(params);
}
template<typename Kernel_traits>
__global__ void flash_bwd_clear_dkvaccum_kernel(const Flash_bwd_params params) {
flash::clear_dKVaccum<Kernel_traits>(params);
}
template<typename Kernel_traits>
__global__ void flash_bwd_convert_dq_kernel(const Flash_bwd_params params, const int nsplits) {
flash::convert_dQ<Kernel_traits>(params, nsplits);
}
template<typename Kernel_traits>
__global__ void flash_bwd_convert_dkv_kernel(const Flash_bwd_params params) {
flash::convert_dKV<Kernel_traits>(params);
}
template<typename Kernel_traits, bool Is_dropout>
void run_flash_bwd_seqk_parallel(Flash_bwd_params &params, cudaStream_t stream) {
const int num_m_block = (params.seqlen_q + Kernel_traits::kBlockM - 1) / Kernel_traits::kBlockM;
dim3 grid_m(num_m_block, params.b, params.h);
const int num_n_block = (params.seqlen_k + Kernel_traits::kBlockN - 1) / Kernel_traits::kBlockN;
int gridDimx = num_n_block;
if (params.deterministic) {
auto dprops = at::cuda::getCurrentDeviceProperties();
gridDimx = (dprops->multiProcessorCount + params.b * params.h - 1) / (params.b * params.h);
}
dim3 grid_n(gridDimx, params.b, params.h);
if (!params.deterministic) {
flash_bwd_dot_do_o_kernel<true, Kernel_traits><<<grid_m, Kernel_traits::kNThreads, 0, stream>>>(params);
} else {
flash_bwd_dot_do_o_kernel<false, Kernel_traits><<<grid_m, Kernel_traits::kNThreads, 0, stream>>>(params);
}
C10_CUDA_KERNEL_LAUNCH_CHECK();
// We want to specialize to is_even_MN and not just is_even_M, since in the case where N is not
// a multiple of kBlockN, we'll need to apply mask in the loop.
const bool is_even_MN = params.cu_seqlens_q == nullptr && params.cu_seqlens_k == nullptr && params.seqlen_q % Kernel_traits::kBlockM == 0 && params.seqlen_k % Kernel_traits::kBlockN == 0;
const bool is_even_K = params.d == Kernel_traits::kHeadDim;
constexpr int smem_size_dq_dk_dv = Kernel_traits::kSmemSize1colblock;
// printf("smem_size_dq_dk_dv = %d\n", smem_size_dq_dk_dv);
BOOL_SWITCH(params.is_causal, Is_causal, [&] {
BOOL_SWITCH(is_even_MN, IsEvenMNConst, [&] {
EVENK_SWITCH(is_even_K, IsEvenKConst, [&] {
LOCAL_SWITCH((params.window_size_left >= 0 || params.window_size_right >= 0) && !params.is_causal, Is_local, [&] {
ALIBI_SWITCH(params.alibi_slopes_ptr != nullptr, Has_alibi, [&] {
// If not IsEvenKConst, we also set IsEvenMNConst to false to reduce number of templates.
// If head dim > 128, set IsEvenMNConst to false to reduce number of templates
// If Is_local, set Is_causal to false
auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, Is_dropout, Is_causal, Is_local && !Is_causal, Has_alibi, IsEvenMNConst && IsEvenKConst && !Is_local && Kernel_traits::kHeadDim <= 128, IsEvenKConst>;
// auto kernel = &flash_bwd_dq_dk_dv_loop_seqk_parallel_kernel<Kernel_traits, false, Is_causal, false, false, true, true>;
if (smem_size_dq_dk_dv >= 48 * 1024) {
C10_CUDA_CHECK(cudaFuncSetAttribute(
kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size_dq_dk_dv));
}
kernel<<<grid_n, Kernel_traits::kNThreads, smem_size_dq_dk_dv, stream>>>(params);
C10_CUDA_KERNEL_LAUNCH_CHECK();
});
});
});
});
});
auto kernel_dq = &flash_bwd_convert_dq_kernel<Kernel_traits>;
if (Kernel_traits::kSmemdQSize >= 48 * 1024) {
C10_CUDA_CHECK(cudaFuncSetAttribute(
kernel_dq, cudaFuncAttributeMaxDynamicSharedMemorySize, Kernel_traits::kSmemdQSize));
}
kernel_dq<<<grid_m, Kernel_traits::kNThreads, Kernel_traits::kSmemdQSize, stream>>>(params, !params.deterministic ? 1 : gridDimx);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
template<typename Kernel_traits, bool Is_dropout>
void run_flash_bwd(Flash_bwd_params &params, cudaStream_t stream) {
#ifndef FLASHATTENTION_DISABLE_BACKWARD
run_flash_bwd_seqk_parallel<Kernel_traits, Is_dropout>(params, stream);
#endif
}
template<typename T>
void run_mha_bwd_hdim32(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 32;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
if (max_smem_per_block >= 2 * ((3 * 128 + 2 * 128) * Headdim + 2 * 128 * 128)) { // 104 KB
if constexpr(!Is_dropout) { // We can afford more registers to keep V in registers
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, true, false, T>, Is_dropout>(params, stream);
} else {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream);
}
} else { // 96 KB
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, true, false, T>, Is_dropout>(params, stream);
}
});
}
template<typename T>
void run_mha_bwd_hdim64(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 64;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
// printf("max_smem_per_block = %d\n", max_smem_per_block);
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
// Changing AtomLayoutMdQ from 2 to 4 takes the same time
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, false, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, true, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 2, 4, 4, false, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, false, false, T>, Is_dropout>(params, stream);
// This is slightly faster. We want to split M more so we need fewer registers to store LSE.
if (max_smem_per_block >= 144 * 1024) {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream);
// This has a lot of register spilling
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, true, false, T>, Is_dropout>(params, stream);
} else {
// if (params.h == params.h_k) {
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>, Is_dropout>(params, stream);
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, false, false, T>, Is_dropout>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, true, false, T>, Is_dropout>(params, stream);
// } else {
// }
}
});
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 4, true, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 4, 2, 2, 2, true, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 32, 128, 4, 1, 4, 1, false, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 16, 128, 4, 1, 4, 1, false, false, T>>(params, stream);
// M=128, N=64 is quite slow, I think because we need to read/write dQaccum twice as many times
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 2, 2, 2, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 4, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 4, 4, 2, 4, false, false, T>>(params, stream);
}
template<typename T>
void run_mha_bwd_hdim96(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 96;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
// printf("max_smem_per_block = %d\n", max_smem_per_block);
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
if (max_smem_per_block >= 116 * 1024) {
if constexpr(!Is_dropout) { // 92KB
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream);
} else { // 116 KB
// This is faster for dropout since we don't have many registers to spare
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>, Is_dropout>(params, stream);
}
} else {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, true, false, T>, Is_dropout>(params, stream);
}
});
}
template<typename T>
void run_mha_bwd_hdim128(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 128;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
// printf("max_smem_per_block = %d\n", max_smem_per_block);
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 32, 128, 8, 2, 2, 2, false, false, T>>(params, stream);
// This is faster, in the case of sequence-parallel bwd (where we need fewer registers).
// Out of these three, the 2nd one is slightly faster (2% faster than the first). Idk why.
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 2, 2, false, false, T>>(params, stream);
if (max_smem_per_block >= 144 * 1024) {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, false, false, T>, Is_dropout>(params, stream);
// run_flash_bwd_seqk_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream);
// run_flash_bwd_seqk_parallel<Flash_bwd_kernel_traits<Headdim, 128, 128, 8, 4, 4, 4, false, true, T>, Is_dropout>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 2, true, false, T>, Is_dropout>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 2, 2, true, false, T>, Is_dropout>(params, stream);
} else {
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream);
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, true, false, T>, Is_dropout>(params, stream);
}
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 128, 8, 2, 4, 4, false, false, T>>(params, stream);
// run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 128, 64, 8, 4, 4, 4, false, false, T>>(params, stream);
});
}
template<typename T>
void run_mha_bwd_hdim160(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 160;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
if (max_smem_per_block >= 116 * 1024) {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream);
} else {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 4, 4, false, true, T>, Is_dropout>(params, stream);
}
});
}
template<typename T>
void run_mha_bwd_hdim192(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 192;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
if (max_smem_per_block >= 136 * 1024) {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream);
} else {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, true, true, T>, Is_dropout>(params, stream);
}
});
}
template<typename T>
void run_mha_bwd_hdim224(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 224;
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 4, 4, false, false, T>, Is_dropout>(params, stream);
});
}
template<typename T>
void run_mha_bwd_hdim256(Flash_bwd_params &params, cudaStream_t stream) {
constexpr static int Headdim = 256;
int device;
cudaGetDevice(&device);
int max_smem_per_block;
cudaError status_ = cudaDeviceGetAttribute(
&max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
if (status_ != cudaSuccess) {
C10_CUDA_CHECK(status_);
}
DROPOUT_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
if (max_smem_per_block >= 176 * 1024) { // H100
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, false, false, T>, Is_dropout>(params, stream);
} else if (max_smem_per_block >= 144 * 1024) { // A100, we don't do double buffering to save smem
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 64, 8, 4, 2, 2, false, true, T>, Is_dropout>(params, stream);
} else { // sm86 and sm89, max smem is 99 KB. Only works without dropout. V in regs and no double buffering.
if constexpr (!Is_dropout) {
run_flash_bwd<Flash_bwd_kernel_traits<Headdim, 64, 32, 8, 4, 1, 2, true, true, T>, false>(params, stream);
}
}
});
}
csrc/flash_attn/src/flash_bwd_preprocess_kernel.h deleted 100644 → 0
View file @ 06c23892
/***************************************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#pragma once
#include <cute/algorithm/copy.hpp>
#include <cutlass/cutlass.h>
#include <cutlass/array.h>
#include <cutlass/numeric_types.h>
#include "block_info.h"
#include "kernel_traits.h"
#include "utils.h"
namespace flash {
using namespace cute;
////////////////////////////////////////////////////////////////////////////////////////////////////
template <int THREADS_PER_ROW, typename Engine0, typename Layout0, typename Engine1, typename Layout1>
inline __device__ void dot_do_o(Tensor<Engine0, Layout0> const &do_, Tensor<Engine0, Layout0> const &o,
Tensor<Engine1, Layout1> &dP_sum, const int gdP_col_stride, const float scale) {
static_assert(Layout0::rank == 3, "Only support 3D Tensor");
static_assert(Layout1::rank == 1, "Only support 1D Tensor");
CUTE_STATIC_ASSERT_V(do_.layout() == o.layout());
// Reshape do_ and o from (8, kBlockM / 32, kHeadDim / 64) to (kBlockM / 32, 8 * kHeadDim / 64)
// The last coordinate is the "page".
Tensor do_reshaped = make_tensor(do_.data(), make_layout(get<1>(do_.layout()),
make_layout(get<0>(do_.layout()),
get<2>(do_.layout()))));
Tensor o_reshaped = make_tensor(o.data(), do_reshaped.layout());
Tensor do_fp32 = flash::convert_type<float>(do_reshaped);
Tensor o_fp32 = flash::convert_type<float>(o_reshaped);
#pragma unroll
for (int mi = 0; mi < size<0>(do_reshaped); ++mi) {
float dP_sum_cur = do_fp32(mi, 0) * o_fp32(mi, 0);
#pragma unroll
for (int ni = 1; ni < size<1>(do_reshaped); ni++) {
dP_sum_cur += do_fp32(mi, ni) * o_fp32(mi, ni);
}
flash::SumOp<float> sum_op;
dP_sum_cur = flash::Allreduce<THREADS_PER_ROW>::run(dP_sum_cur, sum_op) * scale;
if (threadIdx.x % THREADS_PER_ROW == 0) {
dP_sum(mi * gdP_col_stride + threadIdx.x / THREADS_PER_ROW) = dP_sum_cur;
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Just compute dot(do, o) and write the result (softmax_d) to global memory as a separate kernel.
// This is used in the case where we want to parallelize the backward across seqlen_k.
template<bool Clear_dQaccum=true, typename Kernel_traits, typename Params>
inline __device__ void compute_dot_do_o(const Params &params) {
using Element = typename Kernel_traits::Element;
using ElementAccum = typename Kernel_traits::ElementAccum;
using index_t = typename Kernel_traits::index_t;
const int m_block = blockIdx.x;
// The block index for the batch.
const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.z;
// The thread index.
const int tidx = threadIdx.x;
constexpr int kBlockM = Kernel_traits::kBlockM;
constexpr int kHeadDim = Kernel_traits::kHeadDim;
const BlockInfo binfo(params, bidb);
if (m_block * kBlockM >= binfo.actual_seqlen_q) return;
const index_t row_offset_do = binfo.q_offset(params.do_batch_stride, params.do_row_stride, bidb)
+ m_block * kBlockM * params.do_row_stride + bidh * params.do_head_stride;
const index_t row_offset_o = binfo.q_offset(params.o_batch_stride, params.o_row_stride, bidb)
+ m_block * kBlockM * params.o_row_stride + bidh * params.o_head_stride;
const index_t row_offset_dq_accum = binfo.q_offset(params.seqlen_q_rounded * params.h * params.d_rounded, params.h * params.d_rounded, bidb)
+ (m_block * kBlockM + (params.cu_seqlens_q == nullptr ? 0 : 128 * bidb)) * params.h * params.d_rounded + bidh * params.d_rounded;
const index_t row_offset_dpsum = (bidb * params.h + bidh) * params.seqlen_q_rounded + m_block * kBlockM;
Tensor gdO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.do_ptr) + row_offset_do),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.do_row_stride, _1{}));
Tensor gO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.o_ptr) + row_offset_o),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.o_row_stride, _1{}));
Tensor gdQaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dq_accum_ptr) + row_offset_dq_accum),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.h * params.d_rounded, _1{}));
Tensor dP_sum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dsoftmax_sum) + row_offset_dpsum),
Shape<Int<kBlockM>>{}, Stride<_1>{});
typename Kernel_traits::GmemTiledCopydO gmem_tiled_copy_dO;
auto gmem_thr_copy_dO = gmem_tiled_copy_dO.get_thread_slice(tidx);
// TODO: careful, we're zeroing out dQaccum with type float4, but when
// we do atomicAdds, we use type float. The layouts are different. Check this.
typename Kernel_traits::GmemTiledCopydQaccum gmem_tiled_copy_dQaccum;
auto gmem_thr_copy_dQaccum = gmem_tiled_copy_dQaccum.get_thread_slice(tidx);
Tensor tdOgdO = gmem_thr_copy_dO.partition_S(gdO);
Tensor tdOgO = gmem_thr_copy_dO.partition_S(gO);
Tensor tdQgdQaccum = gmem_thr_copy_dQaccum.partition_D(gdQaccum);
Tensor cdO = make_identity_tensor(Shape<Int<kBlockM>, Int<kHeadDim>>{}); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor tdOcdO = gmem_thr_copy_dO.partition_S(cdO);
// Allocate predicate tensors for k
Tensor tdOpdO = make_tensor<bool>(make_shape(size<2>(tdOgdO)));
// Set predicates for k bounds
#pragma unroll
for (int k = 0; k < size(tdOpdO); ++k) {tdOpdO(k) = get<1>(tdOcdO(0, 0, k)) < params.d;}
Tensor tdOrdO = make_fragment_like(tdOgdO);
Tensor tdOrO = make_fragment_like(tdOgO);
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgdO, tdOrdO, tdOcdO, tdOpdO, binfo.actual_seqlen_q - m_block * kBlockM
);
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/true>(
gmem_tiled_copy_dO, tdOgO, tdOrO, tdOcdO, tdOpdO, binfo.actual_seqlen_q - m_block * kBlockM
);
// By right we need to scale dP up by 1/p_dropout, but instead we don't and only scale the final
// results (dQ and dK) by 1/p_dropout. So we need to keep dP_sum scaled down by p_dropout here,
// so that (dP - dP_sum) is on the same scale.
dot_do_o<Kernel_traits::kGmemThreadsPerRow>(tdOrdO, tdOrO, dP_sum,
Kernel_traits::kNThreads / (Kernel_traits::kGmemThreadsPerRow), params.p_dropout);
if (Clear_dQaccum) {
// We're actually not zero'ing out all of dQaccum, but only the part that we're going to
// do atomicAdds on.
Tensor zero = make_fragment_like(tdQgdQaccum);
clear(zero);
cute::copy(gmem_tiled_copy_dQaccum, zero, tdQgdQaccum);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template<typename Kernel_traits, typename Params>
inline __device__ void clear_dKVaccum(const Params &params) {
using ElementAccum = typename Kernel_traits::ElementAccum;
using index_t = typename Kernel_traits::index_t;
const int n_block = blockIdx.x;
// The block index for the batch.
const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.z;
// The thread index.
const int tidx = threadIdx.x;
constexpr int kBlockN = Kernel_traits::kBlockN;
constexpr int kHeadDim = Kernel_traits::kHeadDim;
const BlockInfo binfo(params, bidb);
if (n_block * kBlockN >= binfo.actual_seqlen_k) return;
const index_t row_offset_dkv_accum = ((bidb * params.h_k + bidh) * params.seqlen_k_rounded + n_block * kBlockN) * params.d_rounded;
Tensor gdKaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dk_accum_ptr) + row_offset_dkv_accum),
Shape<Int<kBlockN>, Int<kHeadDim>>{}, Stride<Int<kHeadDim>, _1>{});
Tensor gdVaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dv_accum_ptr) + row_offset_dkv_accum),
Shape<Int<kBlockN>, Int<kHeadDim>>{}, Stride<Int<kHeadDim>, _1>{});
typename Kernel_traits::GmemTiledCopydQaccum gmem_tiled_copy_dKVaccum;
auto gmem_thr_copy_dKVaccum = gmem_tiled_copy_dKVaccum.get_thread_slice(tidx);
Tensor tdKgdKaccum = gmem_thr_copy_dKVaccum.partition_D(gdKaccum);
Tensor tdVgdVaccum = gmem_thr_copy_dKVaccum.partition_D(gdVaccum);
Tensor zero = make_fragment_like(tdKgdKaccum);
clear(zero);
cute::copy(gmem_tiled_copy_dKVaccum, zero, tdKgdKaccum);
cute::copy(gmem_tiled_copy_dKVaccum, zero, tdVgdVaccum);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Convert dQ from dQaccum (in float) to fp16/bf16.
// This is used in the case where we want to parallelize the backward across seqlen_k.
template<typename Kernel_traits, typename Params>
inline __device__ void convert_dQ(const Params &params, const int nsplits) {
using Element = typename Kernel_traits::Element;
using ElementAccum = typename Kernel_traits::ElementAccum;
using index_t = typename Kernel_traits::index_t;
// Shared memory.
extern __shared__ char smem_[];
const int m_block = blockIdx.x;
// The block index for the batch.
const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.z;
// The thread index.
const int tidx = threadIdx.x;
constexpr int kBlockM = Kernel_traits::kBlockM;
constexpr int kHeadDim = Kernel_traits::kHeadDim;
const BlockInfo binfo(params, bidb);
if (m_block * kBlockM >= binfo.actual_seqlen_q) return;
const index_t row_offset_dq = binfo.q_offset(params.dq_batch_stride, params.dq_row_stride, bidb)
+ m_block * kBlockM * params.dq_row_stride + bidh * params.dq_head_stride;
const index_t row_offset_dq_accum = binfo.q_offset(params.seqlen_q_rounded * params.h * params.d_rounded, params.h * params.d_rounded, bidb)
+ (m_block * kBlockM + (params.cu_seqlens_q == nullptr ? 0 : 128 * bidb)) * params.h * params.d_rounded + bidh * params.d_rounded;
Tensor gdQ = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dq_ptr) + row_offset_dq),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.dq_row_stride, _1{}));
Tensor gdQaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dq_accum_ptr) + row_offset_dq_accum),
Shape<Int<kBlockM>, Int<kHeadDim>>{},
make_stride(params.h * params.d_rounded, _1{}));
Tensor sdQ = make_tensor(make_smem_ptr(reinterpret_cast<Element *>(smem_)),
typename Kernel_traits::SmemLayoutdQ{});
typename Kernel_traits::GmemTiledCopydQ gmem_tiled_copy_dQ;
auto gmem_thr_copy_dQ = gmem_tiled_copy_dQ.get_thread_slice(tidx);
typename Kernel_traits::GmemTiledCopydQaccumAtomicAdd gmem_tiled_copy_dQaccum;
auto gmem_thr_copy_dQaccum = gmem_tiled_copy_dQaccum.get_thread_slice(tidx);
typename Kernel_traits::TiledMmadQ tiled_mma_dq;
auto smem_tiled_copy_dQ = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomdQ{}, tiled_mma_dq);
auto smem_thr_copy_dQ = smem_tiled_copy_dQ.get_thread_slice(tidx);
Tensor taccdQsdQ = smem_thr_copy_dQ.partition_D(sdQ); // ((Atom,AtomNum),PIPE_M,PIPE_N)
Tensor tdQsdQ = gmem_thr_copy_dQ.partition_S(sdQ); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdQgdQ = gmem_thr_copy_dQ.partition_D(gdQ);
Tensor tdQgdQaccum = gmem_thr_copy_dQaccum.partition_S(gdQaccum);
Tensor acc_dq = partition_fragment_C(tiled_mma_dq, Shape<Int<kBlockM>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
CUTE_STATIC_ASSERT_V(size(acc_dq) == size(tdQgdQaccum));
Tensor tdQrdQaccum = make_fragment_like(tdQgdQaccum);
clear(acc_dq);
for (int s = 0; s < nsplits; ++s) {
cute::copy(gmem_tiled_copy_dQaccum, tdQgdQaccum, tdQrdQaccum);
#pragma unroll
for (int i = 0; i < size(acc_dq); ++i) { acc_dq(i) += tdQrdQaccum(i); }
tdQgdQaccum.data() = tdQgdQaccum.data() + params.dq_accum_split_stride;
}
#pragma unroll
for (int i = 0; i < size(acc_dq); ++i) { acc_dq(i) *= params.scale_softmax_rp_dropout; }
// Convert acc_dq from fp32 to fp16
Tensor rdQ = flash::convert_type<Element>(acc_dq);
Tensor taccdQrdQ = smem_thr_copy_dQ.retile_S(rdQ); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_dQ, taccdQrdQ, taccdQsdQ);
__syncthreads();
Tensor tdQrdQ = make_tensor<Element>(shape(tdQgdQ));
cute::copy(gmem_tiled_copy_dQ, tdQsdQ, tdQrdQ);
Tensor cdQ = make_identity_tensor(Shape<Int<kBlockM>, Int<kHeadDim>>{}); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor tdQcdQ = gmem_thr_copy_dQ.partition_D(cdQ);
Tensor tdQpdQ = make_tensor<bool>(make_shape(size<2>(tdQgdQ)));
#pragma unroll
for (int k = 0; k < size(tdQpdQ); ++k) { tdQpdQ(k) = get<1>(tdQcdQ(0, 0, k)) < params.d; }
// Clear_OOB_K must be false since we don't want to write zeros to gmem
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dQ, tdQrdQ, tdQgdQ, tdQcdQ, tdQpdQ, binfo.actual_seqlen_q - m_block * kBlockM
);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Convert dK and dV from dKaccum and dVaccum (in float) to fp16/bf16.
// This is used in the case where we want to parallelize the backward across seqlen_q.
template<typename Kernel_traits, typename Params>
inline __device__ void convert_dKV(const Params &params) {
using Element = typename Kernel_traits::Element;
using ElementAccum = typename Kernel_traits::ElementAccum;
using index_t = typename Kernel_traits::index_t;
// Shared memory.
extern __shared__ char smem_[];
const int n_block = blockIdx.x;
// The block index for the batch.
const int bidb = blockIdx.y;
// The block index for the head.
const int bidh = blockIdx.z;
// The thread index.
const int tidx = threadIdx.x;
constexpr int kBlockN = Kernel_traits::kBlockN;
constexpr int kHeadDim = Kernel_traits::kHeadDim;
const BlockInfo binfo(params, bidb);
if (n_block * kBlockN >= binfo.actual_seqlen_k) return;
const index_t row_offset_dk = binfo.k_offset(params.dk_batch_stride, params.dk_row_stride, bidb)
+ n_block * kBlockN * params.dk_row_stride + bidh * params.dk_head_stride;
const index_t row_offset_dv = binfo.k_offset(params.dv_batch_stride, params.dv_row_stride, bidb)
+ n_block * kBlockN * params.dv_row_stride + bidh * params.dv_head_stride;
const index_t row_offset_dkv_accum = ((bidb * params.h_k + bidh) * params.seqlen_k_rounded
+ n_block * kBlockN) * params.d_rounded;
Tensor gdK = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dk_ptr) + row_offset_dk),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.dk_row_stride, _1{}));
Tensor gdV = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.dv_ptr) + row_offset_dv),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
make_stride(params.dv_row_stride, _1{}));
Tensor gdKaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dk_accum_ptr) + row_offset_dkv_accum),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
Stride<Int<kHeadDim>, _1>{});
Tensor gdVaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum *>(params.dv_accum_ptr) + row_offset_dkv_accum),
Shape<Int<kBlockN>, Int<kHeadDim>>{},
Stride<Int<kHeadDim>, _1>{});
Tensor sdK = make_tensor(make_smem_ptr(reinterpret_cast<Element *>(smem_)),
typename Kernel_traits::SmemLayoutdKV{});
Tensor sdV = make_tensor(sdK.data() + size(sdK), typename Kernel_traits::SmemLayoutdKV{}); // (SMEM_N, SMEM_K)
typename Kernel_traits::GmemTiledCopydQ gmem_tiled_copy_dKV;
auto gmem_thr_copy_dKV = gmem_tiled_copy_dKV.get_thread_slice(tidx);
typename Kernel_traits::GmemTiledCopydQaccumAtomicAdd gmem_tiled_copy_dKVaccum;
auto gmem_thr_copy_dKVaccum = gmem_tiled_copy_dKVaccum.get_thread_slice(tidx);
typename Kernel_traits::TiledMmadKV tiled_mma_dkv;
auto smem_tiled_copy_dKV = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomdKV{}, tiled_mma_dkv);
auto smem_thr_copy_dKV = smem_tiled_copy_dKV.get_thread_slice(tidx);
Tensor taccdKsdK = smem_thr_copy_dKV.partition_D(sdK); // ((Atom,AtomNum),PIPE_M,PIPE_N)
Tensor taccdVsdV = smem_thr_copy_dKV.partition_D(sdV); // ((Atom,AtomNum),PIPE_M,PIPE_N)
Tensor tdKsdK = gmem_thr_copy_dKV.partition_S(sdK); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdKgdK = gmem_thr_copy_dKV.partition_D(gdK);
Tensor tdVsdV = gmem_thr_copy_dKV.partition_S(sdV); // ((Atom,AtomNum),ATOM_M,ATOM_N)
Tensor tdVgdV = gmem_thr_copy_dKV.partition_D(gdV);
Tensor tdKgdKaccum = gmem_thr_copy_dKVaccum.partition_S(gdKaccum);
Tensor tdVgdVaccum = gmem_thr_copy_dKVaccum.partition_S(gdVaccum);
Tensor acc_dk = partition_fragment_C(tiled_mma_dkv, Shape<Int<kBlockN>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
Tensor acc_dv = partition_fragment_C(tiled_mma_dkv, Shape<Int<kBlockN>, Int<kHeadDim>>{}); // MMA, MMA_N, MMA_K
CUTE_STATIC_ASSERT_V(size(acc_dk) == size(tdKgdKaccum));
CUTE_STATIC_ASSERT_V(size(acc_dv) == size(tdVgdVaccum));
Tensor tdKrdKaccum = make_fragment_like(tdKgdKaccum);
Tensor tdVrdVaccum = make_fragment_like(tdVgdVaccum);
cute::copy(gmem_tiled_copy_dKVaccum, tdKgdKaccum, tdKrdKaccum);
cute::copy(gmem_tiled_copy_dKVaccum, tdVgdVaccum, tdVrdVaccum);
#pragma unroll
for (int i = 0; i < size(acc_dk); ++i) {
acc_dk(i) = tdKrdKaccum(i) * params.scale_softmax_rp_dropout;
}
#pragma unroll
for (int i = 0; i < size(acc_dv); ++i) {
acc_dv(i) = tdVrdVaccum(i) * params.rp_dropout;
}
// Convert acc_dk from fp32 to fp16
Tensor rdK = flash::convert_type<Element>(acc_dk);
Tensor rdV = flash::convert_type<Element>(acc_dv);
Tensor taccdKrdK = smem_thr_copy_dKV.retile_S(rdK); // ((Atom,AtomNum), MMA_N, MMA_N)
Tensor taccdVrdV = smem_thr_copy_dKV.retile_S(rdV); // ((Atom,AtomNum), MMA_N, MMA_N)
cute::copy(smem_tiled_copy_dKV, taccdKrdK, taccdKsdK);
cute::copy(smem_tiled_copy_dKV, taccdVrdV, taccdVsdV);
__syncthreads();
Tensor tdKrdK = make_tensor<Element>(shape(tdKgdK));
Tensor tdVrdV = make_tensor<Element>(shape(tdVgdV));
cute::copy(gmem_tiled_copy_dKV, tdKsdK, tdKrdK);
cute::copy(gmem_tiled_copy_dKV, tdVsdV, tdVrdV);
Tensor cdKV = make_identity_tensor(Shape<Int<kBlockN>, Int<kHeadDim>>{}); // (BLK_M,BLK_K) -> (blk_m,blk_k)
Tensor tdKVcdKV = gmem_thr_copy_dKV.partition_D(cdKV);
Tensor tdKVpdKV = make_tensor<bool>(make_shape(size<2>(tdKgdK)));
#pragma unroll
for (int k = 0; k < size(tdKVpdKV); ++k) { tdKVpdKV(k) = get<1>(tdKVcdKV(0, 0, k)) < params.d; }
// Clear_OOB_K must be false since we don't want to write zeros to gmem
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdKrdK, tdKgdK, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
);
flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
gmem_tiled_copy_dKV, tdVrdV, tdVgdV, tdKVcdKV, tdKVpdKV, binfo.actual_seqlen_k - n_block * kBlockN
);
}
} // namespace flash
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment