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fix: support static cache graph replay #468

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fix: support static cache graph replay #468

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29 changes: 25 additions & 4 deletions csrc/engine/compiler/static_batching_compiler.cpp
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#include "static_batching_compiler.hpp"
#include "../../cache/cache.hpp"
#include "../../global_state/global_state.hpp"
#include "../../utils.hpp"

namespace infinilm::engine {
StaticBatchingCompiler::StaticBatchingCompiler(const std::shared_ptr<InfinilmModel> &model, RankBarrier *barrier)
Expand All @@ -13,10 +14,22 @@ void StaticBatchingCompiler::compile() {
InfinilmModel::Input input;
input.input_ids = infinicore::Tensor::empty({b, 1}, infinicore::DataType::I64, infinicore::context::getDevice());
input.position_ids = infinicore::Tensor::empty({b, 1}, infinicore::DataType::I64, infinicore::context::getDevice());
input.past_sequence_lengths = infinicore::Tensor::empty({b}, infinicore::DataType::I64, infinicore::context::getDevice());
input.total_sequence_lengths = infinicore::Tensor::empty({b}, infinicore::DataType::I64, infinicore::context::getDevice());
std::vector<int64_t> total_sequence_lengths_vec(b, 1);
infinicore::context::memcpyH2D(input.total_sequence_lengths.value()->data(), total_sequence_lengths_vec.data(), b * sizeof(int64_t), false);
input.past_sequence_lengths = infinicore::Tensor::empty({b}, infinicore::DataType::I32, infinicore::context::getDevice());
input.total_sequence_lengths = infinicore::Tensor::empty({b}, infinicore::DataType::I32, infinicore::context::getDevice());
input.block_tables = infinicore::Tensor::empty({b, 1}, infinicore::DataType::I32, infinicore::context::getDevice());

set_zeros(input.input_ids.value());
set_zeros(input.position_ids.value());
set_zeros(input.past_sequence_lengths.value());

std::vector<int32_t> total_sequence_lengths_vec(b, 1);
infinicore::context::memcpyH2D(input.total_sequence_lengths.value()->data(), total_sequence_lengths_vec.data(), b * sizeof(int32_t), false);

std::vector<int32_t> block_tables_vec(b);
for (size_t i = 0; i < b; ++i) {
block_tables_vec[i] = static_cast<int32_t>(i);
}
infinicore::context::memcpyH2D(input.block_tables.value()->data(), block_tables_vec.data(), b * sizeof(int32_t), false);

// Attention reads attn_metadata from thread-local forward context.
infinilm::global_state::get_forward_context().attn_metadata = {
Expand All @@ -28,7 +41,14 @@ void StaticBatchingCompiler::compile() {
input.slot_mapping,
};

model_->forward(input);
infinicore::context::syncStream();
model_->reset_runtime_state();
infinicore::context::syncStream();

barrier_->wait();
model_->reset_runtime_state();
infinicore::context::syncStream();
infinicore::context::startGraphRecording();
auto output = model_->forward(input);
auto graph = infinicore::context::stopGraphRecording();
Expand All @@ -54,6 +74,7 @@ StaticBatchingCompiler::Compiled StaticBatchingCompiler::get_compiled(
graph_input.position_ids.value()->copy_from(input.position_ids.value());
graph_input.past_sequence_lengths.value()->copy_from(input.past_sequence_lengths.value());
graph_input.total_sequence_lengths.value()->copy_from(input.total_sequence_lengths.value());
model_->reset_runtime_state();

auto graph = std::get<0>(result->second.compiled);
auto shared_output = std::shared_ptr<InfinilmModel::Output>(new InfinilmModel::Output{std::get<1>(result->second.compiled)->logits->resume_from_blob_()});
Expand Down
52 changes: 24 additions & 28 deletions csrc/layers/attention/backends/static_attn.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
#include "infinicore/ops.hpp"
#include "infinicore/ops/per_tensor_dequant_i8.hpp"
#include "infinicore/ops/per_tensor_quant_i8.hpp"
#include <optional>

namespace infinilm::layers::attention::backends {

Expand Down Expand Up @@ -55,25 +56,32 @@ infinicore::Tensor StaticAttentionImpl::forward(const AttentionLayer &layer,
// v_total : [bs, n_kv_head, max_seq_len, head_dim]
auto [k_total, v_total] = do_kv_cache_update(layer, k_permuted, v_permuted, kv_cache, past_sequence_lengths.value());

if (infinilm::quantization::KVQuantAlgo::NONE != this->kv_quant_scheme_) {
infinilm::KVQuantUtils::dequantize(
k_total, v_total,
this->kv_quant_scheme_,
k_scale,
v_scale,
q_reshaped);
}

infinicore::Tensor attn_output;
if (false) {
// experimental nineoothed flash attention
attn_output = infinicore::op::flash_attention(q_reshaped, k_total, v_total, total_sequence_lengths.value(), scale_, true);
attn_output = attn_output->permute({0, 2, 1, 3})
->contiguous()
->view({batch_size, seq_len, num_heads_ * head_dim_}); // [bs, seq_len, n_q_head * head_dim]
if (attn_metadata.block_tables.has_value() && seq_len == 1) {
auto query = q_rope->contiguous()->view({batch_size * seq_len, num_heads_, head_dim_});
auto out = infinicore::Tensor::empty({batch_size * seq_len, num_heads_, head_dim_}, query->dtype(), query->device());
infinicore::op::paged_attention_(
out,
query,
k_total,
v_total,
attn_metadata.block_tables.value(),
total_sequence_lengths.value(),
std::nullopt,
scale_);
attn_output = out->view({batch_size, seq_len, num_heads_ * head_dim_});
} else {
size_t total_seq_len = reinterpret_cast<int32_t *>(total_sequence_lengths.value()->to(infinicore::Device::cpu())->data())[0];

if (infinilm::quantization::KVQuantAlgo::NONE != this->kv_quant_scheme_) {
infinilm::KVQuantUtils::dequantize(
k_total, v_total,
this->kv_quant_scheme_,
k_scale,
v_scale,
q_reshaped);
}

k_total = k_total->narrow({{2, 0, total_seq_len}}); // [bs, n_kv_head, total_seq_len, head_dim]
v_total = v_total->narrow({{2, 0, total_seq_len}}); // [bs, n_kv_head, total_seq_len, head_dim]

Expand Down Expand Up @@ -107,25 +115,13 @@ std::tuple<infinicore::Tensor, infinicore::Tensor> StaticAttentionImpl::do_kv_ca
const infinicore::Tensor past_sequence_lengths) const {

auto batch_size = key->size(0);
auto update_len = key->size(2);
auto k_cache_layer = kv_cache->narrow({{0, 0, 1}})->squeeze(0);
auto v_cache_layer = kv_cache->narrow({{0, 1, 1}})->squeeze(0);

size_t max_batch_size = k_cache_layer->size(0);
size_t max_seq_len = k_cache_layer->size(2);
auto device = k_cache_layer->device();

ASSERT_EQ(batch_size, max_batch_size);

size_t cache_pos = reinterpret_cast<int32_t *>(past_sequence_lengths->to(infinicore::Device::cpu())->data())[0];
auto result_len = cache_pos + update_len;
ASSERT(result_len <= max_seq_len);

auto k_cache_update = k_cache_layer->narrow({{2, cache_pos, update_len}});
auto v_cache_update = v_cache_layer->narrow({{2, cache_pos, update_len}});

k_cache_update->copy_from(key);
v_cache_update->copy_from(value);
infinicore::op::kv_caching_(k_cache_layer, v_cache_layer, key, value, past_sequence_lengths);

return {k_cache_layer, v_cache_layer};
}
Expand Down