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Unverified Commit 7be5d113 authored by Li, Jiang's avatar Li, Jiang Committed by GitHub
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[CPU] Refactor CPU W8A8 scaled_mm (#23071) 


Signed-off-by: default avatarjiang1.li <jiang1.li@intel.com>
parent b029de99
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Showing with 1527 additions and 1275 deletions
.buildkite/scripts/hardware_ci/run-cpu-test.sh
View file @ 7be5d113
......@@ -46,6 +46,11 @@ function cpu_tests() {
set -e
python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
# Run kernel tests
docker exec cpu-test-"$NUMA_NODE" bash -c "
set -e
pytest -v -s tests/kernels/test_onednn.py"
# Run basic model test
docker exec cpu-test-"$NUMA_NODE" bash -c "
set -e
......@@ -99,4 +104,4 @@ function cpu_tests() {
# All of CPU tests are expected to be finished less than 40 mins.
export -f cpu_tests
timeout 1.5h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
timeout 2h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
cmake/cpu_extension.cmake
View file @ 7be5d113
......@@ -182,17 +182,17 @@ endif()
#
# Build oneDNN for W8A8 GEMM kernels (only for x86-AVX512 /ARM platforms)
# Flag to enable ACL kernels for AARCH64 platforms
if ( VLLM_BUILD_ACL STREQUAL "ON")
if (VLLM_BUILD_ACL STREQUAL "ON")
set(USE_ACL ON)
else()
set(USE_ACL OFF)
endif()
if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND OR POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
FetchContent_Declare(
oneDNN
GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
GIT_TAG v3.8.1
GIT_TAG v3.9
GIT_PROGRESS TRUE
GIT_SHALLOW TRUE
)
......@@ -204,7 +204,7 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
endif()
set(ONEDNN_AARCH64_USE_ACL "ON")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
endif()
endif()
set(ONEDNN_LIBRARY_TYPE "STATIC")
set(ONEDNN_BUILD_DOC "OFF")
......@@ -217,38 +217,23 @@ if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
set(ONEDNN_ENABLE_ITT_TASKS "OFF")
set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
set(ONEDNN_VERBOSE "OFF")
set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
FetchContent_MakeAvailable(oneDNN)
list(APPEND LIBS dnnl)
elseif(POWER10_FOUND)
FetchContent_Declare(
oneDNN
GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
GIT_TAG v3.7.2
GIT_PROGRESS TRUE
GIT_SHALLOW TRUE
add_library(dnnl_ext OBJECT "csrc/cpu/dnnl_helper.cpp")
target_include_directories(
dnnl_ext
PUBLIC ${oneDNN_SOURCE_DIR}/include
PUBLIC ${oneDNN_BINARY_DIR}/include
PRIVATE ${oneDNN_SOURCE_DIR}/src
)
set(ONEDNN_LIBRARY_TYPE "STATIC")
set(ONEDNN_BUILD_DOC "OFF")
set(ONEDNN_BUILD_EXAMPLES "OFF")
set(ONEDNN_BUILD_TESTS "OFF")
set(ONEDNN_ENABLE_WORKLOAD "INFERENCE")
set(ONEDNN_ENABLE_PRIMITIVE "MATMUL;REORDER")
set(ONEDNN_BUILD_GRAPH "OFF")
set(ONEDNN_ENABLE_JIT_PROFILING "OFF")
set(ONEDNN_ENABLE_ITT_TASKS "OFF")
set(ONEDNN_ENABLE_MAX_CPU_ISA "OFF")
set(ONEDNN_ENABLE_CPU_ISA_HINTS "OFF")
set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
set(DNNL_CPU_RUNTIME "OMP")
FetchContent_MakeAvailable(oneDNN)
list(APPEND LIBS dnnl)
target_link_libraries(dnnl_ext dnnl)
target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS} -fPIC)
list(APPEND LIBS dnnl_ext)
set(USE_ONEDNN ON)
else()
set(USE_ONEDNN OFF)
endif()
message(STATUS "CPU extension compile flags: ${CXX_COMPILE_FLAGS}")
......@@ -275,7 +260,6 @@ set(VLLM_EXT_SRC
if (AVX512_FOUND AND NOT AVX512_DISABLED)
set(VLLM_EXT_SRC
"csrc/cpu/quant.cpp"
"csrc/cpu/shm.cpp"
${VLLM_EXT_SRC})
if (ENABLE_AVX512BF16 AND ENABLE_AVX512VNNI)
......@@ -289,14 +273,11 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
${VLLM_EXT_SRC})
add_compile_definitions(-DCPU_CAPABILITY_AVX512)
endif()
elseif(POWER10_FOUND)
set(VLLM_EXT_SRC
"csrc/cpu/quant.cpp"
${VLLM_EXT_SRC})
endif()
if (ASIMD_FOUND)
if(USE_ONEDNN)
set(VLLM_EXT_SRC
"csrc/cpu/quant.cpp"
"csrc/cpu/dnnl_kernels.cpp"
${VLLM_EXT_SRC})
endif()
......
csrc/cpu/cpu_types_x86.hpp
View file @ 7be5d113
......@@ -89,7 +89,7 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
explicit FP16Vec16(const FP32Vec16&);
void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
void save(void* ptr, const int elem_num) const {
constexpr uint32_t M = 0xFFFFFFFF;
......@@ -126,7 +126,7 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
explicit BF16Vec16(const FP32Vec16&);
void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
void save(void* ptr, const int elem_num) const {
constexpr uint32_t M = 0xFFFFFFFF;
......@@ -180,8 +180,8 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
(__m128i)vec8_data.reg, 1)) {}
void save(void* ptr) const {
*reinterpret_cast<__m256i*>(ptr) = reg_low;
*reinterpret_cast<__m256i*>((__m256i*)ptr + 1) = reg_high;
_mm256_storeu_si256((__m256i*)ptr, reg_low);
_mm256_storeu_si256((__m256i*)ptr + 1, reg_high);
}
};
#endif
......
csrc/cpu/dnnl_helper.cpp 0 → 100644
View file @ 7be5d113
#include <list>
#include <optional>
#include "common/memory_desc.hpp"
#include "common/memory.hpp"
#include "dnnl_helper.h"
static dnnl::engine& default_engine() {
static dnnl::engine engine(dnnl::engine::kind::cpu, 0);
return engine;
}
static dnnl::stream& default_stream() {
static dnnl::stream stream(default_engine());
return stream;
}
void release_dnnl_matmul_handler(int64_t handler) {
DNNLMatMulPrimitiveHandler* ptr =
reinterpret_cast<DNNLMatMulPrimitiveHandler*>(handler);
delete ptr;
}
template <typename KT, typename VT>
class DNNLPrimitiveCache {
public:
using cache_value_t = std::pair<KT, VT>;
using result_value_t = VT;
using container_t = std::list<cache_value_t>;
using value_iterator_t = typename container_t::iterator;
using map_t = std::unordered_map<KT, value_iterator_t>;
using creator_t = VT (*)();
public:
DNNLPrimitiveCache(size_t capacity)
: capacity_(capacity),
values_(),
key_to_value_(std::min(256lu, capacity)) {
assert(capacity > 0);
}
template <typename F>
result_value_t get_or_create(const KT& key, F&& creator) {
std::optional<value_iterator_t> value = get_value(key);
if (value.has_value()) {
return value.value()->second;
} else {
return add_value({key, creator()})->second;
}
}
size_t size() const { return values_.size(); }
private:
void dump_data() {
std::stringstream ss;
ss << "table_id: " << std::hex << reinterpret_cast<size_t>(this) << std::dec
<< "\n";
ss << "container: [";
for (auto&& iter : values_) {
ss << "(" << iter.first << ", " << std::hex
<< reinterpret_cast<size_t>(iter.second.get()) << "), " << std::dec;
}
ss << "]\n";
ss << "map: [";
for (auto&& iter : key_to_value_) {
ss << "(" << iter.first << ", " << iter.second->first << ", " << std::hex
<< reinterpret_cast<size_t>(iter.second->second.get()) << std::dec
<< "), ";
}
ss << "]\n";
std::printf("%s\n", ss.str().c_str());
}
value_iterator_t add_value(cache_value_t&& new_value) {
if (size() == capacity_) {
cache_value_t& last_item = values_.back();
key_to_value_.erase(last_item.first);
values_.pop_back();
}
auto& added_value_ = values_.emplace_front(std::move(new_value));
key_to_value_.emplace(added_value_.first, values_.begin());
return values_.begin();
}
std::optional<value_iterator_t> get_value(const KT& key) {
if (key_to_value_.size() > 0 && key == values_.begin()->first) {
return values_.begin();
}
auto value_map_iterator = key_to_value_.find(key);
if (value_map_iterator != key_to_value_.end()) {
values_.splice(values_.begin(), values_, value_map_iterator->second);
return value_map_iterator->second;
} else {
return {};
}
}
private:
const size_t capacity_;
container_t values_;
map_t key_to_value_;
};
DNNLMatMulPrimitiveHandler::DNNLMatMulPrimitiveHandler(
const Args& args, dnnl::memory::data_type b_type)
: b_n_size_(args.b_n_size),
b_n_stride_(args.b_n_stride),
b_k_size_(args.b_k_size),
b_k_stride_(args.b_k_stride),
b_type_(b_type),
c_type_(args.c_type),
runtime_memory_ptrs_(8),
primitive_cache_size_(args.primitive_cache_size) {
assert(primitive_cache_size_ > 0);
}
void DNNLMatMulPrimitiveHandler::prepack_weight(
void* original_b_ptr, dnnl::memory::desc b_target_mem_desc) {
dnnl::memory::desc original_b_md({b_k_size_, b_n_size_}, b_type_,
{b_k_stride_, b_n_stride_});
dnnl::memory original_weight(original_b_md, default_engine(), original_b_ptr);
dnnl::memory packed_weight(b_target_mem_desc, default_engine());
{
dnnl::reorder(original_weight, packed_weight)
.execute(default_stream(), original_weight, packed_weight);
default_stream().wait();
}
memory_cache_[DNNL_ARG_WEIGHTS] = packed_weight;
b_target_mem_desc_ = b_target_mem_desc;
}
void DNNLMatMulPrimitiveHandler::set_runtime_memory_ptr(
size_t index, dnnl_memory* memory_ptr) {
dnnl::impl::memory_storage_t* mem_storage_ptr = memory_ptr->memory_storage();
dnnl_memory_desc* mem_desc = const_cast<dnnl_memory_desc*>(memory_ptr->md());
runtime_memory_ptrs_[index] = {mem_storage_ptr, mem_desc};
}
std::pair<dnnl::impl::memory_storage_t*, dnnl_memory_desc*>
DNNLMatMulPrimitiveHandler::get_runtime_memory_ptr(size_t index) {
return runtime_memory_ptrs_[index];
}
namespace std {
template <>
struct hash<W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey> {
size_t operator()(
const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& val) const {
return hash<dnnl_dim_t>()(val.b_n_size) ^ hash<dnnl_dim_t>()(val.b_k_size) ^
hash<int>()(static_cast<int>(val.a_qs)) ^
hash<int>()(static_cast<int>(val.b_qs)) ^ hash<bool>()(val.use_azp) ^
hash<int>()(static_cast<int>(val.c_type));
}
};
template <>
struct hash<W8A8MatMulPrimitiveHandler::MSizeCacheKey> {
size_t operator()(
const W8A8MatMulPrimitiveHandler::MSizeCacheKey& val) const {
return hash<dnnl_dim_t>()(val.a_m_size) ^ hash<bool>()(val.use_bias) ^
hash<int>()(static_cast<int>(val.bias_type));
}
};
} // namespace std
bool operator==(const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& l,
const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& r) {
return l.b_n_size == r.b_n_size && l.b_k_size == r.b_k_size &&
l.a_qs == r.a_qs && l.b_qs == r.b_qs && l.use_azp == r.use_azp &&
l.c_type == r.c_type;
}
bool operator==(const W8A8MatMulPrimitiveHandler::MSizeCacheKey& l,
const W8A8MatMulPrimitiveHandler::MSizeCacheKey& r) {
return l.use_bias == r.use_bias && l.a_m_size == r.a_m_size &&
l.bias_type == r.bias_type;
}
static std::shared_ptr<W8A8MatMulPrimitiveHandler::MSizeCache>
get_w8a8_class_primitive_cache(
const W8A8MatMulPrimitiveHandler::ClassMatmulCacheKey& key,
int64_t cache_size) {
static W8A8MatMulPrimitiveHandler::ClassMatmulCache cache(128);
assert(cache_size > 0);
return cache.get_or_create(key, [&]() {
return std::make_shared<W8A8MatMulPrimitiveHandler::MSizeCache>(cache_size);
});
}
W8A8MatMulPrimitiveHandler::W8A8MatMulPrimitiveHandler(const Args& args)
: DNNLMatMulPrimitiveHandler(
static_cast<const DNNLMatMulPrimitiveHandler::Args&>(args),
dnnl::memory::data_type::s8),
use_azp_(args.use_a_zero_point),
a_qs_(args.a_quantization_strategy),
b_qs_(args.b_quantization_strategy),
m_size_cache_(nullptr) {
assert(a_qs_ != QuantizationStrategy::PER_OUTPUT_CHANNEL);
assert(b_qs_ != QuantizationStrategy::PER_TOKEN);
if (a_qs_ == QuantizationStrategy::PER_TOKEN) {
assert(!use_azp_);
};
prepack_weight(args.b_ptr,
create_primitive_desc(
MSizeCacheKey{.a_m_size = DNNL_RUNTIME_DIM_VAL,
.use_bias = false,
.bias_type = dnnl::memory::data_type::undef},
true)
.weights_desc());
init_runtime_memory_cache(args);
}
void W8A8MatMulPrimitiveHandler::execute(ExecArgs& args) {
auto&& [a_storage, a_mem_desc] = get_runtime_memory_ptr(0);
auto&& [c_storage, c_mem_desc] = get_runtime_memory_ptr(1);
a_storage->set_data_handle((void*)args.a_ptr);
a_mem_desc->dims[0] = args.a_m_size;
c_storage->set_data_handle((void*)args.c_ptr);
c_mem_desc->dims[0] = args.a_m_size;
if (a_qs_ == QuantizationStrategy::PER_TENSOR) {
auto&& [a_scale_storage, a_scale_mem_desc] = get_runtime_memory_ptr(2);
a_scale_storage->set_data_handle((void*)args.a_scales_ptr);
}
if (use_azp_) {
auto&& [a_zero_point_storage, a_zero_point_mem_desc] =
get_runtime_memory_ptr(3);
a_zero_point_storage->set_data_handle((void*)args.a_zero_points_ptr);
}
if (args.use_bias) {
auto&& [bias_storage, bias_mem_desc] = get_runtime_memory_ptr(4);
bias_storage->set_data_handle((void*)args.bias_ptr);
}
dnnl::matmul matmul = get_matmul_cache(args);
matmul.execute(default_stream(), memory_cache_);
default_stream().wait();
}
dnnl::matmul W8A8MatMulPrimitiveHandler::get_matmul_cache(
const MSizeCacheKey& key) {
if (m_size_cache_.get() == nullptr) {
ClassMatmulCacheKey key = {.b_n_size = b_n_size_,
.b_k_size = b_k_size_,
.a_qs = a_qs_,
.b_qs = b_qs_,
.use_azp = use_azp_,
.c_type = c_type_};
m_size_cache_ = get_w8a8_class_primitive_cache(key, primitive_cache_size_);
}
return m_size_cache_->get_or_create(key, [&]() {
dnnl::matmul::primitive_desc desc = this->create_primitive_desc(key, false);
return dnnl::matmul(desc);
});
}
void W8A8MatMulPrimitiveHandler::init_runtime_memory_cache(const Args& args) {
memory_cache_[DNNL_ARG_SRC] = dnnl::memory({{1, b_k_size_},
dnnl::memory::data_type::s8,
dnnl::memory::format_tag::ab},
default_engine(), nullptr);
set_runtime_memory_ptr(0, memory_cache_[DNNL_ARG_SRC].get());
memory_cache_[DNNL_ARG_DST] =
dnnl::memory({{1, b_n_size_}, c_type_, dnnl::memory::format_tag::ab},
default_engine(), nullptr);
set_runtime_memory_ptr(1, memory_cache_[DNNL_ARG_DST].get());
// For PER_TOKEN, scales will be applied in outside epilogue
if (a_qs_ == QuantizationStrategy::PER_TENSOR) {
memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC] = dnnl::memory(
{{1}, dnnl::memory::data_type::f32, {1}}, default_engine(), nullptr);
set_runtime_memory_ptr(
2, memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC].get());
if (use_azp_) {
memory_cache_[DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC] = dnnl::memory(
{{1}, dnnl::memory::data_type::s32, {1}}, default_engine(), nullptr);
set_runtime_memory_ptr(
3, memory_cache_[DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC].get());
}
}
if (b_qs_ == QuantizationStrategy::PER_TENSOR) {
memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS] =
dnnl::memory({{1}, dnnl::memory::data_type::f32, {1}}, default_engine(),
(void*)args.b_scales_ptr);
} else if (b_qs_ == QuantizationStrategy::PER_OUTPUT_CHANNEL) {
memory_cache_[DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS] =
dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
default_engine(), (void*)args.b_scales_ptr);
}
memory_cache_[DNNL_ARG_BIAS] =
dnnl::memory({{b_n_size_}, dnnl::memory::data_type::f32, {1}},
default_engine(), nullptr);
set_runtime_memory_ptr(4, memory_cache_[DNNL_ARG_BIAS].get());
}
dnnl::matmul::primitive_desc W8A8MatMulPrimitiveHandler::create_primitive_desc(
const MSizeCacheKey& key, bool first_time) {
dnnl::memory::desc a_md({key.a_m_size, b_k_size_},
dnnl::memory::data_type::s8,
dnnl::memory::format_tag::ab);
dnnl::memory::desc b_md;
if (first_time) {
b_md =
dnnl::memory::desc({b_k_size_, b_n_size_}, dnnl::memory::data_type::s8,
dnnl::memory::format_tag::any);
} else {
b_md = b_target_mem_desc_;
}
dnnl::memory::desc c_md({key.a_m_size, b_n_size_}, c_type_,
dnnl::memory::format_tag::ab);
dnnl::primitive_attr attr;
// For PER_TOKEN, scales will be applied in outside epilogue
if (a_qs_ == QuantizationStrategy::PER_TENSOR) {
attr.set_scales_mask(DNNL_ARG_SRC, 0);
if (use_azp_) {
attr.set_zero_points_mask(DNNL_ARG_SRC, 0);
}
}
if (b_qs_ == QuantizationStrategy::PER_TENSOR) {
attr.set_scales_mask(DNNL_ARG_WEIGHTS, 0);
} else if (b_qs_ == QuantizationStrategy::PER_OUTPUT_CHANNEL) {
attr.set_scales_mask(DNNL_ARG_WEIGHTS, 2);
}
if (key.use_bias) {
// For PER_TOKEN, bias will be applied in epilogue
assert(a_qs_ == QuantizationStrategy::PER_TENSOR);
dnnl::memory::desc bias_md({1, b_n_size_}, key.bias_type, {b_n_size_, 1});
return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, bias_md,
c_md, attr);
} else {
return dnnl::matmul::primitive_desc(default_engine(), a_md, b_md, c_md,
attr);
}
}
csrc/cpu/dnnl_helper.h 0 → 100644
View file @ 7be5d113
#ifndef DNNL_HELPER_H
#define DNNL_HELPER_H
#include <optional>
#include <cassert>
#include "oneapi/dnnl/dnnl.hpp"
namespace c10 {
struct BFloat16;
struct Half;
} // namespace c10
namespace dnnl {
namespace impl {
struct memory_storage_t;
struct matmul_pd_t;
struct matmul_desc_t;
} // namespace impl
} // namespace dnnl
struct dnnl_memory_desc;
template <typename KT, typename VT>
class DNNLPrimitiveCache;
template <typename T>
struct DNNLType {
static constexpr dnnl::memory::data_type type =
dnnl::memory::data_type::undef;
};
template <>
struct DNNLType<int8_t> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s8;
};
template <>
struct DNNLType<int32_t> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s32;
};
template <>
struct DNNLType<float> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f32;
};
template <>
struct DNNLType<c10::BFloat16> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::bf16;
};
template <>
struct DNNLType<c10::Half> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f16;
};
template <typename T>
constexpr inline dnnl::memory::data_type get_dnnl_type() {
return DNNLType<std::decay_t<T>>::type;
}
class DNNLMatMulPrimitiveHandler {
public:
virtual ~DNNLMatMulPrimitiveHandler() = default;
protected:
struct Args {
dnnl_dim_t b_n_size;
dnnl_dim_t b_n_stride;
dnnl_dim_t b_k_size;
dnnl_dim_t b_k_stride;
void* b_ptr;
dnnl::memory::data_type c_type;
size_t primitive_cache_size;
};
protected:
DNNLMatMulPrimitiveHandler(const Args& args, dnnl::memory::data_type b_type);
void prepack_weight(void* original_b_ptr,
dnnl::memory::desc b_target_mem_desc);
void set_runtime_memory_ptr(size_t index, dnnl_memory* memory_ptr);
std::pair<dnnl::impl::memory_storage_t*, dnnl_memory_desc*>
get_runtime_memory_ptr(size_t index);
protected:
const dnnl_dim_t b_n_size_;
const dnnl_dim_t b_n_stride_;
const dnnl_dim_t b_k_size_;
const dnnl_dim_t b_k_stride_;
dnnl::memory::data_type b_type_;
dnnl::memory::data_type c_type_;
std::unordered_map<int, dnnl::memory> memory_cache_;
std::vector<std::pair<dnnl::impl::memory_storage_t*, dnnl_memory_desc*>>
runtime_memory_ptrs_;
dnnl::memory::desc b_target_mem_desc_;
int64_t primitive_cache_size_;
};
class W8A8MatMulPrimitiveHandler : public DNNLMatMulPrimitiveHandler {
public:
enum class QuantizationStrategy { PER_TOKEN, PER_TENSOR, PER_OUTPUT_CHANNEL };
struct Args : public DNNLMatMulPrimitiveHandler::Args {
bool use_a_zero_point;
QuantizationStrategy a_quantization_strategy;
QuantizationStrategy b_quantization_strategy;
float* b_scales_ptr;
};
struct ClassMatmulCacheKey {
dnnl_dim_t b_n_size;
dnnl_dim_t b_k_size;
QuantizationStrategy a_qs;
QuantizationStrategy b_qs;
bool use_azp;
dnnl::memory::data_type c_type;
friend bool operator==(const ClassMatmulCacheKey& l,
const ClassMatmulCacheKey& r);
};
struct MSizeCacheKey {
dnnl_dim_t a_m_size;
bool use_bias;
dnnl::memory::data_type bias_type;
friend bool operator==(const MSizeCacheKey& l, const MSizeCacheKey& r);
};
using MSizeCache = DNNLPrimitiveCache<MSizeCacheKey, dnnl::matmul>;
using ClassMatmulCache =
DNNLPrimitiveCache<ClassMatmulCacheKey, std::shared_ptr<MSizeCache>>;
struct ExecArgs : public MSizeCacheKey {
const int8_t* a_ptr;
const float* a_scales_ptr;
const int32_t* a_zero_points_ptr;
const void* bias_ptr;
void* c_ptr;
};
public:
W8A8MatMulPrimitiveHandler(const Args& args);
QuantizationStrategy get_input_scale_strategy() const { return a_qs_; }
bool get_input_use_zero_point() const { return use_azp_; }
void execute(ExecArgs& args);
private:
dnnl::matmul::primitive_desc create_primitive_desc(const MSizeCacheKey& key,
bool first_time);
void init_runtime_memory_cache(const Args& args);
dnnl::matmul get_matmul_cache(const MSizeCacheKey& key);
private:
const bool use_azp_;
const QuantizationStrategy a_qs_;
const QuantizationStrategy b_qs_;
std::shared_ptr<MSizeCache> m_size_cache_;
};
#endif
csrc/cpu/dnnl_helper.hpp deleted 100644 → 0
View file @ b029de99
#ifndef DNNL_HELPER_HPP
#define DNNL_HELPER_HPP
#include <c10/util/BFloat16.h>
#include <c10/util/Half.h>
#include "oneapi/dnnl/dnnl.hpp"
namespace {
template <typename T>
struct DNNLType {
static constexpr dnnl::memory::data_type type =
dnnl::memory::data_type::undef;
};
template <>
struct DNNLType<int8_t> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s8;
};
template <>
struct DNNLType<int32_t> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::s32;
};
template <>
struct DNNLType<float> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f32;
};
template <>
struct DNNLType<c10::BFloat16> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::bf16;
};
template <>
struct DNNLType<c10::Half> {
static constexpr dnnl::memory::data_type type = dnnl::memory::data_type::f16;
};
template <typename T>
constexpr inline dnnl::memory::data_type get_dnnl_type() {
return DNNLType<std::decay_t<T>>::type;
}
}; // namespace
template <bool InputNoScale>
class DNNLPrimitiveHelper {
public:
// I8 input GEMM kernel (C = a_scales * A @ (b_scales * B^T) + bias)
// A: [M, K], row-major
// B: [K, N], column-major
// C: [M, N], row-major
// bias: [N], row-major, optional
// a_scales: [MS]
// b_scales: [NS]
// Note: Due to the limitation of oneDNN
// (https://github.com/oneapi-src/oneDNN/issues/1636), the quantized bias is
// not supported.
template <typename OutputT, typename BiasT>
static void gemm_s8s8_jit(const int8_t* a, const int8_t* b, OutputT* c,
const BiasT* bias, dnnl_dim_t M, dnnl_dim_t N,
dnnl_dim_t K, const float* a_scales,
const float* b_scales, dnnl_dim_t MS,
dnnl_dim_t NS) {
auto&& OutputType = get_dnnl_type<OutputT>();
auto&& BiasType = get_dnnl_type<BiasT>();
dnnl::memory::desc a_md({M, K}, dnnl::memory::data_type::s8, {K, 1});
dnnl::memory::desc b_md({K, N}, dnnl::memory::data_type::s8, {1, K});
dnnl::memory::desc c_md({M, N}, OutputType, {N, 1});
dnnl::primitive_attr attr;
if constexpr (!InputNoScale) {
if (MS == 1) {
// per-tensor
attr.set_scales_mask(DNNL_ARG_SRC, 0);
} else {
// per-token
TORCH_CHECK(false, "per-token quantization is unsupported.");
}
}
if (NS == 1) {
// per-tensor
attr.set_scales_mask(DNNL_ARG_WEIGHTS, 0);
} else {
// per-channel
attr.set_scales_mask(DNNL_ARG_WEIGHTS, 2);
}
dnnl::matmul::primitive_desc matmul_pd;
// Create memory descriptors with format_tag::any for the primitive. This
// enables the matmul primitive to choose memory layouts for an
// optimized primitive implementation, and these layouts may differ from the
// ones provided by the user.
#ifdef __aarch64__
auto mat_src_md = dnnl::memory::desc({M, K}, dnnl::memory::data_type::s8,
dnnl::memory::format_tag::any);
auto mat_weights_md = dnnl::memory::desc(
{K, N}, dnnl::memory::data_type::s8, dnnl::memory::format_tag::any);
auto mat_dst_md =
dnnl::memory::desc({M, N}, OutputType, dnnl::memory::format_tag::any);
if (bias) {
dnnl::memory::desc bias_md({1, N}, BiasType, {N, 1});
matmul_pd = dnnl::matmul::primitive_desc(default_engine(), mat_src_md,
mat_weights_md, bias_md,
mat_dst_md, attr);
} else {
matmul_pd = dnnl::matmul::primitive_desc(
default_engine(), mat_src_md, mat_weights_md, mat_dst_md, attr);
}
#else
if (bias) {
dnnl::memory::desc bias_md({1, N}, BiasType, {N, 1});
matmul_pd = dnnl::matmul::primitive_desc(default_engine(), a_md, b_md,
bias_md, c_md, attr);
} else {
matmul_pd = dnnl::matmul::primitive_desc(default_engine(), a_md, b_md,
c_md, attr);
}
#endif
dnnl::matmul matmul(matmul_pd);
auto& engine = default_engine();
dnnl::memory a_m(a_md, engine, (void*)a);
dnnl::memory b_m(b_md, engine, (void*)b);
dnnl::memory c_m(c_md, engine, (void*)c);
dnnl::memory a_scales_m({{MS}, dnnl::memory::data_type::f32, {1}}, engine,
(void*)a_scales);
dnnl::memory b_scales_m({{NS}, dnnl::memory::data_type::f32, {1}}, engine,
(void*)b_scales);
auto& stream = default_stream();
auto mat_src_mem = a_m;
auto mat_weights_mem = b_m;
auto mat_dst_mem = c_m;
#ifdef __aarch64__
if (matmul_pd.weights_desc() != b_m.get_desc()) {
mat_weights_mem = dnnl::memory(matmul_pd.weights_desc(), engine);
dnnl::reorder(b_m, mat_weights_mem).execute(stream, b_m, mat_weights_mem);
}
#endif
if constexpr (InputNoScale) {
if (bias) {
dnnl::memory::desc bias_md({N}, BiasType, {1});
dnnl::memory bias_m(bias_md, engine, (void*)bias);
matmul.execute(
stream, {
{DNNL_ARG_SRC, mat_src_mem},
{DNNL_ARG_WEIGHTS, mat_weights_mem},
{DNNL_ARG_BIAS, bias_m},
{DNNL_ARG_DST, mat_dst_mem},
{DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
});
} else {
matmul.execute(
stream, {
{DNNL_ARG_SRC, mat_src_mem},
{DNNL_ARG_WEIGHTS, mat_weights_mem},
{DNNL_ARG_DST, mat_dst_mem},
{DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
});
}
} else {
if (bias) {
dnnl::memory::desc bias_md({N}, BiasType, {1});
dnnl::memory bias_m(bias_md, engine, (void*)bias);
matmul.execute(
stream, {
{DNNL_ARG_SRC, mat_src_mem},
{DNNL_ARG_WEIGHTS, mat_weights_mem},
{DNNL_ARG_BIAS, bias_m},
{DNNL_ARG_DST, mat_dst_mem},
{DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, a_scales_m},
{DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
});
} else {
matmul.execute(
stream, {
{DNNL_ARG_SRC, mat_src_mem},
{DNNL_ARG_WEIGHTS, mat_weights_mem},
{DNNL_ARG_DST, mat_dst_mem},
{DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, a_scales_m},
{DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, b_scales_m},
});
}
}
stream.wait();
}
private:
static dnnl::engine& default_engine() {
static dnnl::engine engine(dnnl::engine::kind::cpu, 0);
return engine;
}
static dnnl::stream& default_stream() {
static dnnl::stream stream(default_engine());
return stream;
}
};
#endif
csrc/cpu/dnnl_kernels.cpp 0 → 100644
View file @ 7be5d113
#include "cpu_types.hpp"
#include "dnnl_helper.h"
namespace {
template <typename scalar_t>
struct KernelVecType {
using load_vec_type = void;
using cvt_vec_type = void;
};
template <>
struct KernelVecType<float> {
using load_vec_type = vec_op::FP32Vec16;
using cvt_vec_type = vec_op::FP32Vec16;
};
#if !defined(__aarch64__) || defined(ARM_BF16_SUPPORT)
template <>
struct KernelVecType<c10::BFloat16> {
using load_vec_type = vec_op::BF16Vec16;
using cvt_vec_type = vec_op::FP32Vec16;
};
#endif
template <>
struct KernelVecType<c10::Half> {
#if defined(__powerpc64__) || defined(__s390x__)
// Power architecture-specific vector type
using load_vec_type = vec_op::FP32Vec16;
#else
// Fallback for other architectures
using load_vec_type = vec_op::FP16Vec16;
#endif
using cvt_vec_type = vec_op::FP32Vec16;
};
template <bool AZP, typename scalar_t>
void static_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
const float* scale, const int32_t* azp,
const int64_t num_tokens,
const int64_t input_stride,
const int64_t hidden_size) {
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int64_t vec_elem_num = load_vec_t::VEC_ELEM_NUM;
constexpr float i8_min =
static_cast<float>(std::numeric_limits<int8_t>::min());
constexpr float i8_max =
static_cast<float>(std::numeric_limits<int8_t>::max());
const cvt_vec_t inv_scale(1.0 / *scale);
const cvt_vec_t i8_min_vec(i8_min);
const cvt_vec_t i8_max_vec(i8_max);
cvt_vec_t zp_vec;
if constexpr (AZP) {
zp_vec = cvt_vec_t(static_cast<float>(*azp));
}
#pragma omp parallel for
for (int64_t i = 0; i < num_tokens; ++i) {
int64_t j = 0;
const scalar_t* input_ptr = input + i * input_stride;
int8_t* output_ptr = output + i * hidden_size;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input_ptr + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = elems_fp32 * inv_scale;
if constexpr (AZP) {
elems_fp32 = elems_fp32 + zp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output_ptr + j);
}
load_vec_t elems(input_ptr + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = elems_fp32 * inv_scale;
if constexpr (AZP) {
elems_fp32 = elems_fp32 + zp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output_ptr + j, hidden_size - j);
}
}
template <bool AZP, typename scalar_t>
void dynamic_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
float* scale, int32_t* azp,
const int64_t num_tokens,
const int64_t input_stride,
const int64_t hidden_size) {
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
constexpr float i8_min =
static_cast<float>(std::numeric_limits<int8_t>::min());
constexpr float i8_max =
static_cast<float>(std::numeric_limits<int8_t>::max());
const cvt_vec_t i8_min_vec(i8_min);
const cvt_vec_t i8_max_vec(i8_max);
#pragma omp parallel for
for (int64_t i = 0; i < num_tokens; ++i) {
cvt_vec_t max_value(std::numeric_limits<float>::lowest());
cvt_vec_t min_value(std::numeric_limits<float>::max());
{
int64_t j = 0;
const scalar_t* input_ptr = input + i * input_stride;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input_ptr + j);
cvt_vec_t elems_fp32(elems);
if constexpr (AZP) {
max_value = max_value.max(elems_fp32);
min_value = min_value.min(elems_fp32);
} else {
max_value = max_value.max(elems_fp32.abs());
}
}
load_vec_t elems(input_ptr + j);
cvt_vec_t elems_fp32(elems);
if (j + vec_elem_num == hidden_size) {
if constexpr (AZP) {
max_value = max_value.max(elems_fp32);
min_value = min_value.min(elems_fp32);
} else {
max_value = max_value.max(elems_fp32.abs());
}
} else {
if constexpr (AZP) {
max_value = max_value.max(elems_fp32, hidden_size - j);
min_value = min_value.min(elems_fp32, hidden_size - j);
} else {
max_value = max_value.max(elems_fp32.abs(), hidden_size - j);
}
}
}
float scale_val, azp_val;
if constexpr (AZP) {
float max_scalar = max_value.reduce_max();
float min_scalar = min_value.reduce_min();
scale_val = (max_scalar - min_scalar) / 255.0f;
azp_val = std::nearbyint(-128.0f - min_scalar / scale_val);
azp[i] = azp_val;
scale[i] = scale_val;
} else {
scale_val = max_value.reduce_max() / 127.0f;
scale[i] = scale_val;
}
const cvt_vec_t inv_scale(1.0 / scale_val);
const cvt_vec_t azp_vec(azp_val);
{
int64_t j = 0;
const scalar_t* input_ptr = input + i * input_stride;
int8_t* output_ptr = output + i * hidden_size;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input_ptr + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = (elems_fp32 * inv_scale);
if constexpr (AZP) {
elems_fp32 = elems_fp32 + azp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output_ptr + j);
}
load_vec_t elems(input_ptr + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = (elems_fp32 * inv_scale);
if constexpr (AZP) {
elems_fp32 = elems_fp32 + azp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output_ptr + j, hidden_size - j);
}
}
}
template <bool AZP, bool Bias, typename scalar_t>
void dynamic_quant_epilogue(const float* input, scalar_t* output,
const float* a_scale, const int32_t* azp,
const float* azp_adj, const scalar_t* bias,
const int64_t num_tokens,
const int64_t hidden_size) {
CPU_KERNEL_GUARD_IN(dynamic_quant_epilogue)
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
const int64_t thread_num = omp_get_max_threads();
if (num_tokens > thread_num) {
#pragma omp parallel for
for (int64_t i = 0; i < num_tokens; ++i) {
const float* input_ptr = input + i * hidden_size;
scalar_t* output_ptr = output + i * hidden_size;
int64_t j = 0;
cvt_vec_t token_scale_vec(a_scale[i]);
cvt_vec_t token_zp_scale_vec;
if constexpr (AZP) {
float zp_scale_val = a_scale[i] * static_cast<float>(azp[i]);
token_zp_scale_vec = cvt_vec_t(zp_scale_val);
}
for (; j < hidden_size - vec_elem_num; ++j) {
cvt_vec_t elems_fp32(input_ptr + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
cvt_vec_t azp_adj_fp32(azp_adj + j);
elems_fp32 = elems_fp32 - azp_adj_fp32 * token_zp_scale_vec;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + j);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output_ptr + j);
}
cvt_vec_t elems_fp32(input_ptr + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
cvt_vec_t azp_adj_fp32(azp_adj + j);
elems_fp32 = elems_fp32 - azp_adj_fp32 * token_zp_scale_vec;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + j);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output_ptr + j, hidden_size - j);
}
} else {
const int64_t vec_iteration =
(hidden_size + vec_elem_num - 1) / vec_elem_num;
const int64_t vec_iteration_per_thread =
(vec_iteration + thread_num - 1) / thread_num;
const int64_t elem_num_per_thread = vec_iteration_per_thread * vec_elem_num;
#pragma omp parallel for schedule(static, 1)
for (int64_t i = 0; i < thread_num; ++i) {
const int64_t start = elem_num_per_thread * i;
const int64_t end = std::min(hidden_size, elem_num_per_thread + start);
for (int64_t j = 0; j < num_tokens; ++j) {
cvt_vec_t token_scale_vec(a_scale[j]);
cvt_vec_t token_zp_scale_vec;
if constexpr (AZP) {
float zp_scale_val = a_scale[j] * static_cast<float>(azp[j]);
token_zp_scale_vec = cvt_vec_t(zp_scale_val);
}
int64_t k = start;
const float* input_ptr = input + j * hidden_size;
scalar_t* output_ptr = output + j * hidden_size;
for (; k < end - vec_elem_num; k += vec_elem_num) {
cvt_vec_t elems_fp32(input_ptr + k);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
cvt_vec_t azp_adj_fp32(azp_adj + k);
elems_fp32 = elems_fp32 - azp_adj_fp32 * token_zp_scale_vec;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + k);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output_ptr + k);
}
if (k < end) {
cvt_vec_t elems_fp32(input_ptr + k);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
cvt_vec_t azp_adj_fp32(azp_adj + k);
elems_fp32 = elems_fp32 - azp_adj_fp32 * token_zp_scale_vec;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + k);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output_ptr + k, end - k);
}
}
}
}
}
} // namespace
int64_t create_onednn_scaled_mm_handler(
const torch::Tensor& b, // [IC, OC], column-major
const torch::Tensor& b_scales, // [1] or [OC]
at::ScalarType output_type, bool dynamic_act_quant, bool use_azp,
int64_t primitive_cache_size) {
TORCH_CHECK(b.dim() == 2);
TORCH_CHECK(b.stride(0) == 1); // Column-major
TORCH_CHECK(b_scales.is_contiguous());
W8A8MatMulPrimitiveHandler::Args args;
args.primitive_cache_size = primitive_cache_size;
if (b_scales.numel() == 1) {
args.b_quantization_strategy =
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_TENSOR;
} else {
TORCH_CHECK_EQ(b_scales.numel(), b.size(1));
args.b_quantization_strategy =
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_OUTPUT_CHANNEL;
}
args.b_scales_ptr = b_scales.data_ptr<float>();
args.b_k_size = b.size(0);
args.b_k_stride = b.stride(0);
args.b_n_size = b.size(1);
args.b_n_stride = b.stride(1);
args.b_ptr = b.data_ptr<int8_t>();
if (dynamic_act_quant) {
// dynamic per-token, bias, A scales and A zps will be applied in outside.
args.a_quantization_strategy =
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_TOKEN;
args.use_a_zero_point = false;
} else {
// static per-tensor
args.a_quantization_strategy =
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_TENSOR;
args.use_a_zero_point = use_azp;
}
VLLM_DISPATCH_FLOATING_TYPES(output_type, "create_onednn_scaled_mm_handler",
[&] {
if (dynamic_act_quant) {
args.c_type = get_dnnl_type<float>();
} else {
args.c_type = get_dnnl_type<scalar_t>();
}
});
return reinterpret_cast<int64_t>(new W8A8MatMulPrimitiveHandler(args));
}
void onednn_scaled_mm(
torch::Tensor& c, // [M, OC], row-major
const torch::Tensor& a, // [M, IC], row-major
const torch::Tensor& a_scales, // [M] or [1]
const std::optional<torch::Tensor>& azp, // [M] or [1]
const std::optional<torch::Tensor>& azp_adj, // [M] or [1]
const std::optional<torch::Tensor>& bias, // [N]
int64_t handler) {
CPU_KERNEL_GUARD_IN(onednn_scaled_mm)
TORCH_CHECK(a.dim() == 2);
TORCH_CHECK(a.is_contiguous());
TORCH_CHECK(c.is_contiguous());
W8A8MatMulPrimitiveHandler* ptr =
reinterpret_cast<W8A8MatMulPrimitiveHandler*>(handler);
const int32_t* azp_ptr = nullptr;
if (azp.has_value()) {
azp_ptr = azp->data_ptr<int32_t>();
}
if (ptr->get_input_scale_strategy() ==
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_TENSOR) {
TORCH_CHECK_EQ(a_scales.numel(), 1);
}
W8A8MatMulPrimitiveHandler::ExecArgs exec_args;
exec_args.a_ptr = a.data_ptr<int8_t>();
exec_args.a_m_size = a.size(0);
exec_args.bias_ptr = nullptr;
exec_args.use_bias = false;
exec_args.a_scales_ptr = nullptr;
exec_args.a_zero_points_ptr = nullptr;
VLLM_DISPATCH_FLOATING_TYPES(c.scalar_type(), "onednn_scaled_mm", [&] {
if (ptr->get_input_scale_strategy() ==
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_TENSOR) {
if (bias.has_value()) {
exec_args.bias_ptr = bias->data_ptr<scalar_t>();
exec_args.bias_type = get_dnnl_type<scalar_t>();
exec_args.use_bias = true;
}
exec_args.a_scales_ptr = a_scales.data_ptr<float>();
exec_args.a_zero_points_ptr = azp_ptr;
exec_args.c_ptr = c.data_ptr<scalar_t>();
ptr->execute(exec_args);
} else if (ptr->get_input_scale_strategy() ==
W8A8MatMulPrimitiveHandler::QuantizationStrategy::PER_TOKEN) {
torch::Tensor tmp_fp32_out =
torch::empty_like(c, ::at::ScalarType::Float);
exec_args.c_ptr = tmp_fp32_out.data_ptr<float>();
ptr->execute(exec_args);
if (bias.has_value()) {
if (azp.has_value()) {
dynamic_quant_epilogue<true, true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), azp_ptr, azp_adj->data_ptr<float>(),
bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
} else {
dynamic_quant_epilogue<false, true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), azp_ptr, nullptr,
bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
}
} else {
if (azp.has_value()) {
dynamic_quant_epilogue<true, false>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), azp_ptr, azp_adj->data_ptr<float>(),
(scalar_t*)nullptr, c.size(0), c.size(1));
} else {
dynamic_quant_epilogue<false, false>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), azp_ptr, nullptr, (scalar_t*)nullptr,
c.size(0), c.size(1));
}
}
} else {
TORCH_CHECK(false, "invalid act quant type.");
}
});
}
// static-per-tensor quantization.
void static_scaled_int8_quant(
torch::Tensor& out, // [batch, hidden_size]
const torch::Tensor& input, // [batch, hidden_size]
const torch::Tensor& scale, std::optional<torch::Tensor> const& azp) {
CPU_KERNEL_GUARD_IN(static_scaled_int8_quant)
TORCH_CHECK(out.is_contiguous());
TORCH_CHECK_EQ(input.dim(), 2);
TORCH_CHECK_EQ(input.stride(1), 1);
TORCH_CHECK(scale.numel() == 1);
TORCH_CHECK(!azp.has_value() || azp->numel() == 1);
const int64_t stride = input.stride(0);
const int64_t hidden_size = input.size(1);
const int64_t num_tokens = input.size(0);
VLLM_DISPATCH_FLOATING_TYPES(
input.scalar_type(), "static_scaled_int8_quant_impl", [&] {
if (azp.has_value()) {
static_scaled_int8_quant_impl<true>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), azp->data_ptr<int32_t>(), num_tokens,
stride, hidden_size);
} else {
static_scaled_int8_quant_impl<false>(input.data_ptr<scalar_t>(),
out.data_ptr<int8_t>(),
scale.data_ptr<float>(), nullptr,
num_tokens, stride, hidden_size);
}
});
}
// dynamic-per-token quantization.
void dynamic_scaled_int8_quant(
torch::Tensor& out, // [batch, hidden_size]
const torch::Tensor& input, // [batch, hidden_size]
torch::Tensor& scale, // [batch, 1]
std::optional<torch::Tensor> const& azp) {
CPU_KERNEL_GUARD_IN(dynamic_scaled_int8_quant)
TORCH_CHECK(out.is_contiguous());
TORCH_CHECK_EQ(input.dim(), 2);
TORCH_CHECK_EQ(input.stride(1), 1);
const int64_t hidden_size = input.size(1);
const int64_t num_tokens = input.size(0);
const int64_t stride = input.stride(0);
VLLM_DISPATCH_FLOATING_TYPES(
input.scalar_type(), "dynamic_scaled_int8_quant_impl", [&] {
if (azp.has_value()) {
dynamic_scaled_int8_quant_impl<true>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), azp->data_ptr<int32_t>(), num_tokens,
stride, hidden_size);
} else {
dynamic_scaled_int8_quant_impl<false>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), nullptr, num_tokens, stride,
hidden_size);
}
});
}
csrc/cpu/quant.cpp deleted 100644 → 0
View file @ b029de99
#include "cpu_types.hpp"
#include "dnnl_helper.hpp"
namespace {
template <typename scalar_t>
struct KernelVecType {
using load_vec_type = void;
using azp_adj_load_vec_type = void;
using cvt_vec_type = void;
};
template <>
struct KernelVecType<float> {
using load_vec_type = vec_op::FP32Vec16;
using azp_adj_load_vec_type = vec_op::INT32Vec16;
using cvt_vec_type = vec_op::FP32Vec16;
};
#if !defined(__aarch64__) || defined(ARM_BF16_SUPPORT)
template <>
struct KernelVecType<c10::BFloat16> {
using load_vec_type = vec_op::BF16Vec16;
using azp_adj_load_vec_type = vec_op::INT32Vec16;
using cvt_vec_type = vec_op::FP32Vec16;
};
#endif
template <>
struct KernelVecType<c10::Half> {
#if defined(__powerpc64__) || defined(__s390x__)
// Power architecture-specific vector type
using load_vec_type = vec_op::FP32Vec16;
#else
// Fallback for other architectures
using load_vec_type = vec_op::FP16Vec16;
#endif
using azp_adj_load_vec_type = vec_op::INT32Vec16;
using cvt_vec_type = vec_op::FP32Vec16;
};
#if defined(__AVX512F__) || defined(__aarch64__)
template <bool AZP, typename scalar_t>
void static_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
const float* scale, const int32_t* azp,
const int num_tokens,
const int hidden_size) {
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
constexpr float i8_min =
static_cast<float>(std::numeric_limits<int8_t>::min());
constexpr float i8_max =
static_cast<float>(std::numeric_limits<int8_t>::max());
const cvt_vec_t inv_scale(1.0 / *scale);
const cvt_vec_t i8_min_vec(i8_min);
const cvt_vec_t i8_max_vec(i8_max);
cvt_vec_t zp_vec;
if constexpr (AZP) {
zp_vec = cvt_vec_t(static_cast<float>(*azp));
}
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = elems_fp32 * inv_scale;
if constexpr (AZP) {
elems_fp32 = elems_fp32 + zp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j);
}
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = elems_fp32 * inv_scale;
if constexpr (AZP) {
elems_fp32 = elems_fp32 + zp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j, hidden_size - j);
}
}
template <bool AZP, typename scalar_t>
void dynamic_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
float* scale, int32_t* azp,
const int num_tokens,
const int hidden_size) {
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
constexpr float i8_min =
static_cast<float>(std::numeric_limits<int8_t>::min());
constexpr float i8_max =
static_cast<float>(std::numeric_limits<int8_t>::max());
const cvt_vec_t i8_min_vec(i8_min);
const cvt_vec_t i8_max_vec(i8_max);
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
cvt_vec_t max_value(std::numeric_limits<float>::lowest());
cvt_vec_t min_value(std::numeric_limits<float>::max());
{
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
if constexpr (AZP) {
max_value = max_value.max(elems_fp32);
min_value = min_value.min(elems_fp32);
} else {
max_value = max_value.max(elems_fp32.abs());
}
}
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
if (j + vec_elem_num == hidden_size) {
if constexpr (AZP) {
max_value = max_value.max(elems_fp32);
min_value = min_value.min(elems_fp32);
} else {
max_value = max_value.max(elems_fp32.abs());
}
} else {
if constexpr (AZP) {
max_value = max_value.max(elems_fp32, hidden_size - j);
min_value = min_value.min(elems_fp32, hidden_size - j);
} else {
max_value = max_value.max(elems_fp32.abs(), hidden_size - j);
}
}
}
float scale_val, azp_val;
if constexpr (AZP) {
float max_scalar = max_value.reduce_max();
float min_scalar = min_value.reduce_min();
scale_val = (max_scalar - min_scalar) / 255.0f;
azp_val = std::nearbyint(-128.0f - min_scalar / scale_val);
azp[i] = static_cast<int32_t>(azp_val);
scale[i] = scale_val;
} else {
scale_val = max_value.reduce_max() / 127.0f;
scale[i] = scale_val;
}
const cvt_vec_t inv_scale(1.0 / scale_val);
const cvt_vec_t azp_vec(azp_val);
{
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = (elems_fp32 * inv_scale);
if constexpr (AZP) {
elems_fp32 = elems_fp32 + azp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j);
}
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = (elems_fp32 * inv_scale);
if constexpr (AZP) {
elems_fp32 = elems_fp32 + azp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j, hidden_size - j);
}
}
}
template <bool PerChannel, typename scalar_t>
void static_quant_epilogue(const float* input, scalar_t* output,
const float a_scale, const float* b_scale,
const int32_t* azp_with_adj, const int num_tokens,
const int hidden_size) {
CPU_KERNEL_GUARD_IN(dynamic_output_scale_impl)
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using azp_adj_load_vec_t =
typename KernelVecType<scalar_t>::azp_adj_load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
cvt_vec_t a_scale_vec(a_scale);
cvt_vec_t b_scale_vec(*b_scale);
cvt_vec_t scale_vec = a_scale_vec * b_scale_vec;
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
cvt_vec_t elems_fp32(input + i * hidden_size + j);
azp_adj_load_vec_t azp_adj_vec(azp_with_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
if constexpr (PerChannel) {
b_scale_vec = cvt_vec_t(b_scale + j);
scale_vec = b_scale_vec * a_scale_vec;
}
elems_fp32 = elems_fp32 - scale_vec * azp_adj_fp32;
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j);
}
cvt_vec_t elems_fp32(input + i * hidden_size + j);
azp_adj_load_vec_t azp_adj_vec(azp_with_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
if constexpr (PerChannel) {
b_scale_vec = cvt_vec_t(b_scale + j);
scale_vec = b_scale_vec * a_scale_vec;
}
elems_fp32 = elems_fp32 - scale_vec * azp_adj_fp32;
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j, hidden_size - j);
}
}
template <bool AZP, bool PerChannel, bool Bias, typename scalar_t>
void dynamic_quant_epilogue(const float* input, scalar_t* output,
const float* a_scale, const float* b_scale,
const int32_t* azp, const int32_t* azp_adj,
const scalar_t* bias, const int num_tokens,
const int hidden_size) {
CPU_KERNEL_GUARD_IN(dynamic_quant_epilogue)
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using azp_adj_load_vec_t =
typename KernelVecType<scalar_t>::azp_adj_load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
int j = 0;
cvt_vec_t token_scale_vec(a_scale[i]);
cvt_vec_t token_zp_scale_vec;
if constexpr (AZP) {
float zp_scale_val = a_scale[i] * static_cast<float>(azp[i]);
if constexpr (!PerChannel) {
zp_scale_val *= *b_scale;
}
token_zp_scale_vec = cvt_vec_t(zp_scale_val);
}
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
cvt_vec_t elems_fp32(input + i * hidden_size + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
azp_adj_load_vec_t azp_adj_vec(azp_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
azp_adj_fp32 = azp_adj_fp32 * token_zp_scale_vec;
if constexpr (PerChannel) {
cvt_vec_t b_scale_vec(b_scale + j);
azp_adj_fp32 = azp_adj_fp32 * b_scale_vec;
}
elems_fp32 = elems_fp32 - azp_adj_fp32;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + j);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j);
}
cvt_vec_t elems_fp32(input + i * hidden_size + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
azp_adj_load_vec_t azp_adj_vec(azp_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
azp_adj_fp32 = azp_adj_fp32 * token_zp_scale_vec;
if constexpr (PerChannel) {
cvt_vec_t b_scale_vec(b_scale + j);
azp_adj_fp32 = azp_adj_fp32 * b_scale_vec;
}
elems_fp32 = elems_fp32 - azp_adj_fp32;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + j);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j, hidden_size - j);
}
}
#elif defined(__powerpc64__)
template <bool AZP, typename scalar_t>
void static_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
const float* scale, const int32_t* azp,
const int num_tokens,
const int hidden_size) {
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
constexpr float i8_min =
static_cast<float>(std::numeric_limits<int8_t>::min());
constexpr float i8_max =
static_cast<float>(std::numeric_limits<int8_t>::max());
const cvt_vec_t inv_scale(1.0 / *scale);
const cvt_vec_t i8_min_vec(i8_min);
const cvt_vec_t i8_max_vec(i8_max);
cvt_vec_t zp_vec;
if constexpr (AZP) {
zp_vec = cvt_vec_t(static_cast<float>(*azp));
}
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = elems_fp32 * inv_scale;
if constexpr (AZP) {
elems_fp32 = elems_fp32 + zp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j);
}
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = elems_fp32 * inv_scale;
if constexpr (AZP) {
elems_fp32 = elems_fp32 + zp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j, hidden_size - j);
}
}
template <bool AZP, typename scalar_t>
void dynamic_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
float* scale, int32_t* azp,
const int num_tokens,
const int hidden_size) {
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
constexpr float i8_min =
static_cast<float>(std::numeric_limits<int8_t>::min());
constexpr float i8_max =
static_cast<float>(std::numeric_limits<int8_t>::max());
const cvt_vec_t i8_min_vec(i8_min);
const cvt_vec_t i8_max_vec(i8_max);
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
cvt_vec_t max_value(std::numeric_limits<float>::lowest());
cvt_vec_t min_value(std::numeric_limits<float>::max());
{
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
if constexpr (AZP) {
max_value = max_value.max(elems_fp32);
min_value = min_value.min(elems_fp32);
} else {
max_value = max_value.max(elems_fp32.abs());
}
}
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
if (j + vec_elem_num == hidden_size) {
if constexpr (AZP) {
max_value = max_value.max(elems_fp32);
min_value = min_value.min(elems_fp32);
} else {
max_value = max_value.max(elems_fp32.abs());
}
} else {
if constexpr (AZP) {
max_value = max_value.max(elems_fp32, hidden_size - j);
min_value = min_value.min(elems_fp32, hidden_size - j);
} else {
max_value = max_value.max(elems_fp32.abs(), hidden_size - j);
}
}
}
float scale_val, azp_val;
if constexpr (AZP) {
float max_scalar = max_value.reduce_max();
float min_scalar = min_value.reduce_min();
scale_val = (max_scalar - min_scalar) / 255.0f;
azp_val = std::nearbyint(-128.0f - min_scalar / scale_val);
azp[i] = static_cast<int32_t>(azp_val);
scale[i] = scale_val;
} else {
scale_val = max_value.reduce_max() / 127.0f;
scale[i] = scale_val;
}
const cvt_vec_t inv_scale(1.0 / scale_val);
const cvt_vec_t azp_vec(azp_val);
{
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = (elems_fp32 * inv_scale);
if constexpr (AZP) {
elems_fp32 = elems_fp32 + azp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j);
}
load_vec_t elems(input + i * hidden_size + j);
cvt_vec_t elems_fp32(elems);
elems_fp32 = (elems_fp32 * inv_scale);
if constexpr (AZP) {
elems_fp32 = elems_fp32 + azp_vec;
}
elems_fp32 = elems_fp32.clamp(i8_min_vec, i8_max_vec);
vec_op::INT8Vec16 elems_int8(elems_fp32);
elems_int8.save(output + i * hidden_size + j, hidden_size - j);
}
}
}
template <bool PerChannel, typename scalar_t>
void static_quant_epilogue(const float* input, scalar_t* output,
const float a_scale, const float* b_scale,
const int32_t* azp_with_adj, const int num_tokens,
const int hidden_size) {
CPU_KERNEL_GUARD_IN(dynamic_output_scale_impl)
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using azp_adj_load_vec_t =
typename KernelVecType<scalar_t>::azp_adj_load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
cvt_vec_t a_scale_vec(a_scale);
cvt_vec_t b_scale_vec(*b_scale);
cvt_vec_t scale_vec = a_scale_vec * b_scale_vec;
int j = 0;
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
cvt_vec_t elems_fp32(input + i * hidden_size + j);
azp_adj_load_vec_t azp_adj_vec(azp_with_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
if constexpr (PerChannel) {
b_scale_vec = cvt_vec_t(b_scale + j);
scale_vec = b_scale_vec * a_scale_vec;
}
elems_fp32 = elems_fp32 - scale_vec * azp_adj_fp32;
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j);
}
cvt_vec_t elems_fp32(input + i * hidden_size + j);
azp_adj_load_vec_t azp_adj_vec(azp_with_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
if constexpr (PerChannel) {
b_scale_vec = cvt_vec_t(b_scale + j);
scale_vec = b_scale_vec * a_scale_vec;
}
elems_fp32 = elems_fp32 - scale_vec * azp_adj_fp32;
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j, hidden_size - j);
}
}
template <bool AZP, bool PerChannel, bool Bias, typename scalar_t>
void dynamic_quant_epilogue(const float* input, scalar_t* output,
const float* a_scale, const float* b_scale,
const int32_t* azp, const int32_t* azp_adj,
const scalar_t* bias, const int num_tokens,
const int hidden_size) {
CPU_KERNEL_GUARD_IN(dynamic_quant_epilogue)
using load_vec_t = typename KernelVecType<scalar_t>::load_vec_type;
using azp_adj_load_vec_t =
typename KernelVecType<scalar_t>::azp_adj_load_vec_type;
using cvt_vec_t = typename KernelVecType<scalar_t>::cvt_vec_type;
constexpr int vec_elem_num = load_vec_t::VEC_ELEM_NUM;
#pragma omp parallel for
for (int i = 0; i < num_tokens; ++i) {
int j = 0;
cvt_vec_t token_scale_vec(a_scale[i]);
cvt_vec_t token_zp_scale_vec;
if constexpr (AZP) {
float zp_scale_val = a_scale[i] * static_cast<float>(azp[i]);
if constexpr (!PerChannel) {
zp_scale_val *= *b_scale;
}
token_zp_scale_vec = cvt_vec_t(zp_scale_val);
}
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
cvt_vec_t elems_fp32(input + i * hidden_size + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
azp_adj_load_vec_t azp_adj_vec(azp_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
azp_adj_fp32 = azp_adj_fp32 * token_zp_scale_vec;
if constexpr (PerChannel) {
cvt_vec_t b_scale_vec(b_scale + j);
azp_adj_fp32 = azp_adj_fp32 * b_scale_vec;
}
elems_fp32 = elems_fp32 - azp_adj_fp32;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + j);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j);
}
cvt_vec_t elems_fp32(input + i * hidden_size + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
azp_adj_load_vec_t azp_adj_vec(azp_adj + j);
cvt_vec_t azp_adj_fp32(azp_adj_vec);
azp_adj_fp32 = azp_adj_fp32 * token_zp_scale_vec;
if constexpr (PerChannel) {
cvt_vec_t b_scale_vec(b_scale + j);
azp_adj_fp32 = azp_adj_fp32 * b_scale_vec;
}
elems_fp32 = elems_fp32 - azp_adj_fp32;
}
if constexpr (Bias) {
load_vec_t bias_vec(bias + j);
cvt_vec_t bias_vec_fp32(bias_vec);
elems_fp32 = elems_fp32 + bias_vec_fp32;
}
load_vec_t elems_out(elems_fp32);
elems_out.save(output + i * hidden_size + j, hidden_size - j);
}
}
#else
template <typename scalar_t>
void static_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
const float* scale, const int32_t* azp,
const int num_tokens,
const int hidden_size) {
TORCH_CHECK(false,
"static_scaled_int8_quant_impl requires AVX512/powerpc64/AArch64 "
"support.")
}
template <typename scalar_t>
void dynamic_scaled_int8_quant_impl(const scalar_t* input, int8_t* output,
float* scale, int32_t* azp,
const int num_tokens,
const int hidden_size) {
TORCH_CHECK(false,
"dynamic_scaled_int8_quant_impl requires "
"AVX512/powerpc64/AArch64 support.")
}
template <bool PerChannel, typename scalar_t>
void static_quant_epilogue(const float* input, scalar_t* output,
const float a_scale, const float* b_scale,
const int32_t* azp_with_adj, const int num_tokens,
const int hidden_size) {
TORCH_CHECK(
false, "static_quant_epilogue requires AVX512/powerpc64/AArch64 support.")
}
template <typename scalar_t>
void dynamic_quant_epilogue(const float* input, scalar_t* output,
const float* a_scale, const float* b_scale,
const int32_t* azp, const int32_t* azp_with_adj,
const scalar_t* bias, const int num_tokens,
const int hidden_size) {
TORCH_CHECK(
false,
"dynamic_quant_epilogue requires AVX512/powerpc64/AArch64 support.")
}
#endif
} // namespace
void int8_scaled_mm(torch::Tensor& c, // [M, OC], row-major
const torch::Tensor& a, // [M, IC], row-major
const torch::Tensor& b, // [IC, OC], column-major
const torch::Tensor& a_scales, // [1] or [M]
const torch::Tensor& b_scales, // [1] or [OC]
const std::optional<torch::Tensor>& bias // [OC]
) {
CPU_KERNEL_GUARD_IN(cutlass_scaled_mm)
// Checks for conformality
TORCH_CHECK(a.dtype() == torch::kInt8 && b.dtype() == torch::kInt8,
"int8_scaled_mm only supports INT8 inputs.")
TORCH_CHECK(a.dim() == 2 && b.dim() == 2 && c.dim() == 2);
TORCH_CHECK(c.size(0) == a.size(0) && a.size(1) == b.size(0) &&
b.size(1) == c.size(1));
TORCH_CHECK(a_scales.numel() == 1 || a_scales.numel() == a.size(0));
TORCH_CHECK(b_scales.numel() == 1 || b_scales.numel() == b.size(1));
// Check for strides and alignment
TORCH_CHECK(a.stride(1) == 1 && c.stride(1) == 1); // Row-major
TORCH_CHECK(b.stride(0) == 1); // Column-major
TORCH_CHECK(c.stride(0) % 16 == 0 &&
b.stride(1) % 16 == 0); // 16 Byte Alignment
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) {
TORCH_CHECK(bias->numel() == b.size(1) && bias->is_contiguous() &&
bias->dim() == 1);
}
VLLM_DISPATCH_FLOATING_TYPES(c.scalar_type(), "int8_scaled_mm", [&] {
if (a_scales.numel() != 1) {
// per-token
// Note: oneDNN doesn't support per-token activation quantization
// Ideally we want to fuse the GEMM and the scale procedure with oneDNN
// JIT, the intermediate data is cached in registers or L1. But for now
// the oneDNN GEMM code generation only supports two quantization
// patterns: per-tensor or per-output-channel of weight.
// So we have to apply the per-token scale with a 'epilogue'. In C=s_a *
// s_b * (A@B) + bias, the C_inter = s_b * (A@B) is computed by oneDNN
// GEMM, then the per-token scale (and bias) is applied with the epilogue
// C=s_a * C_inter + bias.
torch::Tensor tmp_fp32_out =
torch::empty_like(c, ::at::ScalarType::Float);
// Compute C_inter=s_b * (A@B)
DNNLPrimitiveHelper<true>::gemm_s8s8_jit<float, void>(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(),
tmp_fp32_out.data_ptr<float>(), nullptr, a.size(0), b.size(1),
a.size(1), nullptr, b_scales.data_ptr<float>(), 0, b_scales.numel());
if (bias.has_value()) {
// Compute C=s_a * C_inter + bias
dynamic_quant_epilogue<false, true, true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), nullptr, nullptr, nullptr,
bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
} else {
// Compute C=s_a * C_inter
dynamic_quant_epilogue<false, true, false, scalar_t>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), nullptr, nullptr, nullptr, nullptr,
c.size(0), c.size(1));
}
} else {
// per-tensor
if (bias.has_value()) {
// Compute C=s_a * s_b * (A@B) + bias
DNNLPrimitiveHelper<false>::gemm_s8s8_jit(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(), c.data_ptr<scalar_t>(),
bias->data_ptr<scalar_t>(), a.size(0), b.size(1), a.size(1),
a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
a_scales.numel(), b_scales.numel());
} else {
// Compute C=s_a * s_b * (A@B)
DNNLPrimitiveHelper<false>::gemm_s8s8_jit<scalar_t, void>(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(), c.data_ptr<scalar_t>(),
nullptr, a.size(0), b.size(1), a.size(1),
a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
a_scales.numel(), b_scales.numel());
}
}
});
}
void int8_scaled_mm_azp(torch::Tensor& c, // [M, OC], row-major
const torch::Tensor& a, // [M, IC], row-major
const torch::Tensor& b, // [IC, OC], column-major
const torch::Tensor& a_scales, // [1] or [M]
const torch::Tensor& b_scales, // [1] or [OC]
const torch::Tensor& azp_adj, // [OC]
const std::optional<torch::Tensor>& azp, // [1] or [M]
const std::optional<torch::Tensor>& bias // [OC]
) {
CPU_KERNEL_GUARD_IN(cutlass_scaled_mm_azp)
// Checks for conformality
TORCH_CHECK(a.dtype() == torch::kInt8 && b.dtype() == torch::kInt8,
"int8_scaled_mm_azp only supports INT8 inputs.")
TORCH_CHECK(a.dim() == 2 && b.dim() == 2 && c.dim() == 2);
TORCH_CHECK(c.size(0) == a.size(0) && a.size(1) == b.size(0) &&
b.size(1) == c.size(1));
TORCH_CHECK(a_scales.numel() == 1 || a_scales.numel() == a.size(0));
TORCH_CHECK(b_scales.numel() == 1 || b_scales.numel() == b.size(1));
// Check for strides and alignment
TORCH_CHECK(a.stride(1) == 1 && c.stride(1) == 1); // Row-major
TORCH_CHECK(b.stride(0) == 1); // Column-major
TORCH_CHECK(c.stride(0) % 16 == 0 &&
b.stride(1) % 16 == 0); // 16 Byte Alignment
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) {
TORCH_CHECK(bias->numel() == b.size(1) && bias->is_contiguous());
}
if (azp) {
TORCH_CHECK(azp->numel() == a.size(0) && azp->is_contiguous());
}
TORCH_CHECK(azp_adj.numel() == b.size(1) && azp_adj.is_contiguous());
// azp & bias types
TORCH_CHECK(azp_adj.dtype() == torch::kInt32);
TORCH_CHECK(!azp || azp->dtype() == torch::kInt32);
TORCH_CHECK(!bias || bias->dtype() == c.dtype(),
"currently bias dtype must match output dtype ", c.dtype());
VLLM_DISPATCH_FLOATING_TYPES(c.scalar_type(), "int8_scaled_mm_azp", [&] {
torch::Tensor tmp_fp32_out = torch::empty_like(c, ::at::ScalarType::Float);
if (a_scales.numel() != 1) {
// per-token
// Note: oneDNN doesn't support per-token activation quantization
// Compute C_inter=s_b * (A@B)
DNNLPrimitiveHelper<true>::gemm_s8s8_jit<float, void>(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(),
tmp_fp32_out.data_ptr<float>(), nullptr, a.size(0), b.size(1),
a.size(1), nullptr, b_scales.data_ptr<float>(), 0, b_scales.numel());
if (bias.has_value()) {
// Compute C=s_a * C_inter - s_a * s_b * azp * azp_adj + bias
if (b_scales.numel() != 1) {
// Per-Channel
dynamic_quant_epilogue<true, true, true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
azp->data_ptr<int32_t>(), azp_adj.data_ptr<int32_t>(),
bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
} else {
// Per-Tensor
dynamic_quant_epilogue<true, false, true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
azp->data_ptr<int32_t>(), azp_adj.data_ptr<int32_t>(),
bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
}
} else {
// Compute C=s_a * C_inter - s_a * s_b * azp * azp_adj
if (b_scales.numel() != 1) {
// Per-Channel
dynamic_quant_epilogue<true, true, false, scalar_t>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
azp->data_ptr<int32_t>(), azp_adj.data_ptr<int32_t>(), nullptr,
c.size(0), c.size(1));
} else {
// Per-Tensor
dynamic_quant_epilogue<true, false, false, scalar_t>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
azp->data_ptr<int32_t>(), azp_adj.data_ptr<int32_t>(), nullptr,
c.size(0), c.size(1));
}
}
} else {
// per-tensor
if (bias.has_value()) {
// Compute C_inter=s_a * s_b * (A@B) + bias
DNNLPrimitiveHelper<false>::gemm_s8s8_jit(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(),
tmp_fp32_out.data_ptr<float>(), bias->data_ptr<scalar_t>(),
a.size(0), b.size(1), a.size(1), a_scales.data_ptr<float>(),
b_scales.data_ptr<float>(), a_scales.numel(), b_scales.numel());
} else {
// Compute C_inter=s_a * s_b * (A@B)
DNNLPrimitiveHelper<false>::gemm_s8s8_jit<float, void>(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(),
tmp_fp32_out.data_ptr<float>(), nullptr, a.size(0), b.size(1),
a.size(1), a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
a_scales.numel(), b_scales.numel());
}
// Compute C=C_inter - s_a * s_b * azp_adj
if (b_scales.numel() != 1) {
// Per-Channel
static_quant_epilogue<true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
*a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
azp_adj.data_ptr<int32_t>(), a.size(0), b.size(1));
} else {
// Per-Tensor
static_quant_epilogue<false>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
*a_scales.data_ptr<float>(), b_scales.data_ptr<float>(),
azp_adj.data_ptr<int32_t>(), a.size(0), b.size(1));
}
}
});
}
// static-per-tensor quantization.
void static_scaled_int8_quant(torch::Tensor& out, // [..., hidden_size]
const torch::Tensor& input, // [..., hidden_size]
const torch::Tensor& scale,
std::optional<torch::Tensor> const& azp) {
CPU_KERNEL_GUARD_IN(static_scaled_int8_quant)
TORCH_CHECK(input.is_contiguous());
TORCH_CHECK(out.is_contiguous());
TORCH_CHECK(scale.numel() == 1);
TORCH_CHECK(!azp.has_value() || azp->numel() == 1);
const int hidden_size = input.size(-1);
const int num_tokens = input.numel() / hidden_size;
VLLM_DISPATCH_FLOATING_TYPES(
input.scalar_type(), "static_scaled_int8_quant_impl", [&] {
if (azp.has_value()) {
static_scaled_int8_quant_impl<true>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), azp->data_ptr<int32_t>(), num_tokens,
hidden_size);
} else {
static_scaled_int8_quant_impl<false>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), nullptr, num_tokens, hidden_size);
}
});
}
// dynamic-per-token quantization.
void dynamic_scaled_int8_quant(
torch::Tensor& out, // [..., hidden_size]
const torch::Tensor& input, // [..., hidden_size]
torch::Tensor& scale, // [..., 1]
std::optional<torch::Tensor> const& azp) {
CPU_KERNEL_GUARD_IN(dynamic_scaled_int8_quant)
TORCH_CHECK(input.is_contiguous());
TORCH_CHECK(out.is_contiguous());
int const hidden_size = input.size(-1);
int const num_tokens = input.numel() / hidden_size;
VLLM_DISPATCH_FLOATING_TYPES(
input.scalar_type(), "dynamic_scaled_int8_quant_impl", [&] {
if (azp.has_value()) {
dynamic_scaled_int8_quant_impl<true>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), azp->data_ptr<int32_t>(), num_tokens,
hidden_size);
} else {
dynamic_scaled_int8_quant_impl<false>(
input.data_ptr<scalar_t>(), out.data_ptr<int8_t>(),
scale.data_ptr<float>(), nullptr, num_tokens, hidden_size);
}
});
}
#if defined(__powerpc64__)
void int8_scaled_mm_ppc64le(torch::Tensor& c, // [M, OC], row-major
const torch::Tensor& a, // [M, IC], row-major
const torch::Tensor& b, // [IC, OC], column-major
const torch::Tensor& a_scales,
const torch::Tensor& b_scales,
const std::optional<torch::Tensor>& bias // [OC]
) {
CPU_KERNEL_GUARD_IN(cutlass_scaled_mm)
// Checks for conformality
TORCH_CHECK(a.dtype() == torch::kInt8 && b.dtype() == torch::kInt8,
"int8_scaled_mm_ppc64le only supports INT8 inputs.");
TORCH_CHECK(a.dim() == 2 && b.dim() == 2 && c.dim() == 2);
TORCH_CHECK(c.size(0) == a.size(0) && a.size(1) == b.size(0) &&
b.size(1) == c.size(1));
// We dont need this
TORCH_CHECK(a_scales.numel() == 1 || a_scales.numel() == a.size(0));
TORCH_CHECK(b_scales.numel() == 1 || b_scales.numel() == b.size(1));
// Check for strides and alignment
TORCH_CHECK(a.stride(1) == 1 && c.stride(1) == 1); // Row-major
TORCH_CHECK(b.stride(0) == 1); // Column-major
TORCH_CHECK(c.stride(0) % 16 == 0 &&
b.stride(1) % 16 == 0); // 16 Byte Alignment
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) {
TORCH_CHECK(bias->numel() == b.size(1) && bias->is_contiguous() &&
bias->dim() == 1);
}
VLLM_DISPATCH_FLOATING_TYPES(c.scalar_type(), "int8_scaled_mm_ppc64le", [&] {
torch::Tensor tmp_fp32_out = torch::empty_like(c, ::at::ScalarType::Float);
// Compute C_inter=s_b * (A@B)
DNNLPrimitiveHelper<true>::gemm_s8s8_jit<float, void>(
a.data_ptr<int8_t>(), b.data_ptr<int8_t>(),
tmp_fp32_out.data_ptr<float>(), nullptr, a.size(0), b.size(1),
a.size(1), nullptr, b_scales.data_ptr<float>(), 0, b_scales.numel());
if (bias.has_value()) {
// Compute C=s_a * C_inter + bias
dynamic_quant_epilogue<false, true, true>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), nullptr, nullptr, nullptr,
bias->data_ptr<scalar_t>(), c.size(0), c.size(1));
} else {
// Compute C=s_a * C_inter
dynamic_quant_epilogue<false, true, false, scalar_t>(
tmp_fp32_out.data_ptr<float>(), c.data_ptr<scalar_t>(),
a_scales.data_ptr<float>(), nullptr, nullptr, nullptr, nullptr,
c.size(0), c.size(1));
}
});
}
#endif
csrc/cpu/torch_bindings.cpp
View file @ 7be5d113
......@@ -6,25 +6,20 @@
std::string init_cpu_threads_env(const std::string& cpu_ids);
void int8_scaled_mm(torch::Tensor& c, const torch::Tensor& a,
const torch::Tensor& b, const torch::Tensor& a_scales,
const torch::Tensor& b_scales,
const std::optional<torch::Tensor>& bias);
void int8_scaled_mm_azp(torch::Tensor& c, const torch::Tensor& a,
const torch::Tensor& b, const torch::Tensor& a_scales,
const torch::Tensor& b_scales,
const torch::Tensor& azp_adj,
const std::optional<torch::Tensor>& azp,
const std::optional<torch::Tensor>& bias);
#if defined(__powerpc64__)
void int8_scaled_mm_ppc64le(torch::Tensor& c, const torch::Tensor& a,
const torch::Tensor& b,
const torch::Tensor& a_scales,
const torch::Tensor& b_scales,
const std::optional<torch::Tensor>& bias);
#endif
void release_dnnl_matmul_handler(int64_t handler);
int64_t create_onednn_scaled_mm_handler(const torch::Tensor& b,
const torch::Tensor& b_scales,
at::ScalarType output_type,
bool dynamic_act_quant, bool use_azp,
int64_t primitive_cache_size);
void onednn_scaled_mm(torch::Tensor& c, const torch::Tensor& a,
const torch::Tensor& a_scales,
const std::optional<torch::Tensor>& azp,
const std::optional<torch::Tensor>& azp_adj,
const std::optional<torch::Tensor>& bias,
int64_t handler);
void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
torch::Tensor& kv_cache, double scale,
......@@ -151,8 +146,25 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
// Quantization
#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__))
#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__)) || \
defined(__powerpc64__)
at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
// Helper function to release oneDNN handlers
ops.def("release_dnnl_matmul_handler(int handler) -> ()",
&release_dnnl_matmul_handler);
// Create oneDNN W8A8 handler
ops.def(
"create_onednn_scaled_mm_handler(Tensor b, Tensor b_scales, ScalarType "
"output_type, bool dynamic_act_quant, bool use_azp, int "
"primitive_cache_size) -> int",
&create_onednn_scaled_mm_handler);
// oneDNN scaled_mm for W8A8 with static per-tensor activation quantization
ops.def(
"onednn_scaled_mm(Tensor! c, Tensor a, Tensor a_scales, Tensor? azp, "
"Tensor? azp_adj, Tensor? bias, int handler) -> ()");
ops.impl("onednn_scaled_mm", torch::kCPU, &onednn_scaled_mm);
// Compute int8 quantized tensor for given scaling factor.
ops.def(
......@@ -168,50 +180,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
{stride_tag});
ops.impl("dynamic_scaled_int8_quant", torch::kCPU,
&dynamic_scaled_int8_quant);
// W8A8 GEMM, supporting symmetric per-tensor or per-row/column
// quantization.
ops.def(
"cutlass_scaled_mm(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor? bias) -> ()",
{stride_tag});
ops.impl("cutlass_scaled_mm", torch::kCPU, &int8_scaled_mm);
// w8a8 GEMM, supporting asymmetric per-tensor or per-row/column
// quantization.
ops.def(
"cutlass_scaled_mm_azp(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor azp_adj,"
" Tensor? azp, Tensor? bias) -> ()",
{stride_tag});
ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp);
#elif defined(__powerpc64__)
// Compute int8 quantized tensor for given scaling factor.
ops.def(
"static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale,"
"Tensor? azp) -> ()");
ops.impl("static_scaled_int8_quant", torch::kCPU, &static_scaled_int8_quant);
// Compute int8 quantized tensor and scaling factor
ops.def(
"dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale, "
"Tensor!? azp) -> ()");
ops.impl("dynamic_scaled_int8_quant", torch::kCPU,
&dynamic_scaled_int8_quant);
// W8A8 GEMM, supporting symmetric quantization.
ops.def(
"cutlass_scaled_mm(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor? bias) -> ()");
ops.impl("cutlass_scaled_mm", torch::kCPU, &int8_scaled_mm_ppc64le);
// w8a8 GEMM, supporting asymmetric per-tensor or per-row/column
// quantization.
ops.def(
"cutlass_scaled_mm_azp(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor azp_adj,"
" Tensor? azp, Tensor? bias) -> ()");
ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp);
#endif
// SHM CCL
......
tests/kernels/test_onednn.py 0 → 100644
View file @ 7be5d113
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Integration tests for FlexAttention backend vs default backend"""
from typing import Optional
import pytest
import torch
from tests.kernels.utils import to_int8
from vllm import _custom_ops as ops
from vllm.platforms import current_platform
if not current_platform.is_cpu():
pytest.skip("skipping CPU-only tests", allow_module_level=True)
NK_FACTORS = [
(256, 128),
(4096, 4096),
(16384, 4096),
(1023, 491),
(1001, 15),
]
M_FACTORS = [
(16, 1, 32, 128, 64),
(1, 17, 1, 31, 17),
]
CACHE_SIZES = [2]
DTYPE = [torch.bfloat16]
def rand_int8(shape: tuple, device: str = "cpu"):
return to_int8(torch.rand(shape, device=device) * 255 - 128)
def ref_int8_scaled_mm(
a: torch.Tensor,
b: torch.Tensor,
scale_a: torch.Tensor,
scale_b: torch.Tensor,
azp: Optional[torch.Tensor],
bias: Optional[torch.Tensor],
output_type: torch.dtype,
):
if azp is not None:
a = a.to(dtype=torch.float32) - azp.to(dtype=torch.float32)
output = torch.mm((scale_a * a.to(dtype=torch.float32)),
(scale_b * b.to(dtype=torch.float32)))
if bias is not None:
output += bias.float()
return output.to(dtype=output_type)
def onednn_int8_gemm_test_helper(primitive_cache_size: int,
m: int,
n: int,
k: int,
per_tensor_a_quant: bool,
per_tensor_b_quant: bool,
use_azp: bool,
use_bias: bool,
out_dtype: torch.dtype = torch.bfloat16,
device: str = "cpu"):
# Test for a oneDNN kernel with per-tensor / per-token activation
# quantization and per-tensor / per-output channel weight quantization.
a = to_int8(torch.randn((m, k), device=device) * 5)
b = to_int8(torch.randn((n, k), device=device).t() * 5)
a_scales_shape = (1, 1) if per_tensor_a_quant else (m, 1)
b_scales_shape = (1, 1) if per_tensor_b_quant else (1, n)
scale_a = (torch.randn(a_scales_shape, device=device, dtype=torch.float32))
scale_b = (torch.randn(b_scales_shape, device=device, dtype=torch.float32))
if use_azp:
azp = torch.rand(a_scales_shape, dtype=torch.float32) * 10 + 1.5
azp = (azp / scale_a).round().to(dtype=torch.int32)
azp_adj = scale_b * b.sum(dim=0, keepdim=True, dtype=torch.float32)
else:
azp = None
azp_adj = None
if use_bias:
bias = torch.rand((n, ), device=device, dtype=out_dtype) * 10
else:
bias = None
handler = ops.create_onednn_scaled_mm(
b,
scale_b,
out_dtype,
not per_tensor_a_quant,
use_azp,
primitive_cache_size,
)
out = torch.zeros((m, n), dtype=out_dtype)
ops.onednn_scaled_mm(handler, a, out, scale_a, azp, azp_adj, bias)
baseline = ref_int8_scaled_mm(a, b, scale_a, scale_b, azp, bias, out_dtype)
torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
if use_bias:
# To test runtime bias setting
out = torch.zeros((m, n), dtype=out_dtype)
ops.onednn_scaled_mm(handler, a, out, scale_a, azp, azp_adj, None)
baseline = ref_int8_scaled_mm(a, b, scale_a, scale_b, azp, None,
out_dtype)
torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
@pytest.mark.parametrize("n,k", NK_FACTORS)
@pytest.mark.parametrize("m_list", M_FACTORS)
@pytest.mark.parametrize("per_tensor_a_scale", [True, False])
@pytest.mark.parametrize("per_tensor_b_scale", [True, False])
@pytest.mark.parametrize("use_bias", [True, False])
@pytest.mark.parametrize("use_azp", [True, False])
@pytest.mark.parametrize("output_type", DTYPE)
@pytest.mark.parametrize("primitive_cache_size", CACHE_SIZES)
def test_onednn_int8_scaled_gemm(
n: int,
k: int,
m_list: tuple[int],
per_tensor_a_scale: bool,
per_tensor_b_scale: bool,
use_bias: bool,
use_azp: bool,
output_type: torch.dtype,
primitive_cache_size: int,
):
for m in m_list:
onednn_int8_gemm_test_helper(
primitive_cache_size=primitive_cache_size,
m=m,
n=n,
k=k,
per_tensor_a_quant=per_tensor_a_scale,
per_tensor_b_quant=per_tensor_b_scale,
use_bias=use_bias,
use_azp=use_azp,
out_dtype=output_type,
)
vllm/_custom_ops.py
View file @ 7be5d113
......@@ -1827,3 +1827,86 @@ if hasattr(torch.ops._C, "int8_scaled_mm_with_quant"):
M = mat1.size(0)
N = mat2.size(0)
return torch.empty((M, N), dtype=out_dtype)
class CPUDNNLGEMMHandler:
def __init__(self) -> None:
self.handler: Optional[int] = None
self.n = -1
self.k = -1
def __del__(self):
if self.handler is not None:
torch.ops._C.release_dnnl_matmul_handler(self.handler)
def create_onednn_scaled_mm(
weight: torch.Tensor, # [K, N]
weight_scales: torch.Tensor,
output_type: torch.dtype,
dynamic_quant: bool,
use_azp: bool,
primitive_cache_size: int = 128,
) -> CPUDNNLGEMMHandler:
handler = CPUDNNLGEMMHandler()
handler.k, handler.n = weight.size()
handler.handler = torch.ops._C.create_onednn_scaled_mm_handler(
weight, weight_scales, output_type, dynamic_quant, use_azp,
primitive_cache_size)
return handler
def onednn_scaled_int8_quant(input: torch.Tensor,
scale: Optional[torch.Tensor] = None,
azp: Optional[torch.Tensor] = None,
symmetric: bool = True):
"""
Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.
Args:
input: The input tensor to be quantized to int8.
scale: Optional scaling factor for the int8 quantization.
When not provided, we invoke dynamic-per-token quantization.
azp: Optional zero-point for the int8 quantization.
Must be provided for asymmetric quantization if `scale` is provided.
symmetric: Whether to use symmetric quantization (scale only, azp ignored).
Returns:
tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] : Output int8 tensor, scales, and optionally azp.
"""
output = torch.empty_like(input, dtype=torch.int8)
token_num = input.numel() // input.shape[-1]
input = input.view((token_num, input.shape[-1]))
if scale is not None:
# static-per-tensor quantization.
assert symmetric == (
azp
is None), "azp must only be provided for asymmetric quantization."
torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
return output, scale, azp
# dynamic-per-token quantization.
input_scales = torch.empty((token_num, 1),
device=input.device,
dtype=torch.float32)
input_azp = None if symmetric else torch.empty_like(input_scales,
dtype=torch.int32)
torch.ops._C.dynamic_scaled_int8_quant(output, input, input_scales,
input_azp)
return output, input_scales, input_azp
def onednn_scaled_mm(
dnnl_handler: CPUDNNLGEMMHandler,
x: torch.Tensor,
output: torch.Tensor,
input_scale: Optional[torch.Tensor],
input_zp: Optional[torch.Tensor],
input_zp_adj: Optional[torch.Tensor],
bias: Optional[torch.Tensor],
) -> torch.Tensor:
torch.ops._C.onednn_scaled_mm(output, x, input_scale, input_zp,
input_zp_adj, bias, dnnl_handler.handler)
return output
vllm/model_executor/layers/fused_moe/layer.py
View file @ 7be5d113
......@@ -360,10 +360,15 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
elif current_platform.is_cpu():
if current_platform.get_cpu_architecture() == CpuArchEnum.X86:
from vllm.model_executor.layers.fused_moe import cpu_fused_moe
dtype = layer.w13_weight.dtype
from vllm.model_executor.layers.utils import (
check_cpu_sgl_kernel)
dtype_w13 = layer.w13_weight.dtype
_, n_w13, k_w13 = layer.w13_weight.size()
dtype_w2 = layer.w2_weight.dtype
_, n_w2, k_w2 = layer.w2_weight.size()
if (envs.VLLM_CPU_SGL_KERNEL
and torch._C._cpu._is_amx_tile_supported()
and dtype == torch.bfloat16):
and check_cpu_sgl_kernel(n_w13, k_w13, dtype_w13)
and check_cpu_sgl_kernel(n_w2, k_w2, dtype_w2)):
packed_w13_weight = torch.ops._C.convert_weight_packed(
layer.w13_weight)
assert packed_w13_weight.size() == layer.w13_weight.size()
......
vllm/model_executor/layers/linear.py
View file @ 7be5d113
......@@ -199,11 +199,10 @@ class UnquantizedLinearMethod(LinearMethodBase):
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
if current_platform.is_cpu() and envs.VLLM_CPU_SGL_KERNEL:
from vllm.model_executor.layers.utils import check_cpu_sgl_kernel
N, K = layer.weight.size()
dtype = layer.weight.dtype
if (torch._C._cpu._is_amx_tile_supported()
and dtype == torch.bfloat16 and N % 32 == 0
and K % 32 == 0):
if check_cpu_sgl_kernel(N, K, dtype):
packed_weight = torch.ops._C.convert_weight_packed(
layer.weight)
assert packed_weight.size() == layer.weight.size()
......@@ -215,7 +214,8 @@ class UnquantizedLinearMethod(LinearMethodBase):
else:
logger.warning(
"CPU SGL kernels require Intel AMX support,"
" bfloat16 weight, IC and OC are divisible by 32.")
" bf16/fp16/int8 weight, IC and OC are divisible by "
"32 and 16.")
layer.use_cpu_sgl = False
def apply(self,
......
vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py
View file @ 7be5d113
......@@ -6,6 +6,8 @@ from typing import Optional
from vllm.model_executor.layers.quantization.kernels.scaled_mm.aiter import (
AiterScaledMMLinearKernel)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.cpu import (
CPUScaledMMLinearKernel)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.cutlass import (
CutlassScaledMMLinearKernel)
from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import ( # noqa: E501
......@@ -18,7 +20,7 @@ from vllm.platforms import PlatformEnum, current_platform
# in priority/performance order (when available)
_POSSIBLE_KERNELS: dict[PlatformEnum, list[type[ScaledMMLinearKernel]]] = {
PlatformEnum.CPU: [CutlassScaledMMLinearKernel],
PlatformEnum.CPU: [CPUScaledMMLinearKernel],
PlatformEnum.CUDA: [CutlassScaledMMLinearKernel],
PlatformEnum.ROCM: [AiterScaledMMLinearKernel, TritonScaledMMLinearKernel],
PlatformEnum.TPU: [XLAScaledMMLinearKernel],
......
vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py 0 → 100644
View file @ 7be5d113
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
import torch
from vllm import _custom_ops as ops
from vllm import envs
from vllm.model_executor.layers.quantization.utils import replace_parameter
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
convert_to_channelwise)
from vllm.model_executor.layers.utils import check_cpu_sgl_kernel
from vllm.platforms import current_platform
from vllm.platforms.interface import CpuArchEnum
from .ScaledMMLinearKernel import (ScaledMMLinearKernel,
ScaledMMLinearLayerConfig)
class CPUScaledMMLinearKernel(ScaledMMLinearKernel):
@classmethod
def get_min_capability(cls) -> int:
return 75
@classmethod
def can_implement(
cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
if not current_platform.is_cpu():
return False, "CPUScaledMM requires running on CPU."
return True, None
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
weight = getattr(layer, self.w_q_name)
dtype = weight.dtype
N, K = weight.size()
if (current_platform.get_cpu_architecture() == CpuArchEnum.X86
and envs.VLLM_CPU_SGL_KERNEL and self.config.input_symmetric
and check_cpu_sgl_kernel(N, K, dtype)):
self.linear_method = self._apply_weights_sgl
self.process_weights_for_sgl(layer)
else:
self.linear_method = self._apply_weights_onednn
self.process_weights_for_onednn(layer)
def process_weights_for_onednn(self, layer: torch.nn.Module) -> None:
# WEIGHT
# Transpose to [K, N] for convenience
weight = getattr(layer, self.w_q_name)
replace_parameter(
layer, self.w_q_name,
torch.nn.Parameter(weight.t().data, requires_grad=False))
# WEIGHT SCALE
# oneDNN kernels support only per-tensor and per-channel.
# If we have a fused module (QKV, MLP) with per tensor scales (thus N
# scales being passed to the kernel), convert to the per-channel case.
is_fused_module = len(layer.logical_widths) > 1
weight_scale = getattr(layer, self.w_s_name)
if is_fused_module and not self.config.is_channelwise:
weight_scale = convert_to_channelwise(weight_scale,
layer.logical_widths)
replace_parameter(
layer, self.w_s_name,
torch.nn.Parameter(weight_scale.data, requires_grad=False))
# INPUT SCALE
if self.config.is_static_input_scheme:
input_scale = getattr(layer, self.i_s_name)
if self.config.input_symmetric:
replace_parameter(
layer, self.i_s_name,
torch.nn.Parameter(input_scale.max(), requires_grad=False))
setattr(layer, self.i_zp_name, None)
else:
input_zero_point = getattr(layer, self.i_zp_name)
# reconstruct the ranges
int8_traits = torch.iinfo(torch.int8)
azps = input_zero_point.to(dtype=torch.int32)
range_max = (input_scale * (int8_traits.max - azps)).max()
range_min = (input_scale * (int8_traits.min - azps)).min()
scale = (range_max - range_min) / (int8_traits.max -
int8_traits.min)
replace_parameter(
layer, self.i_s_name,
torch.nn.Parameter(scale, requires_grad=False))
azp = (int8_traits.min -
range_min / scale).round().to(dtype=torch.int32)
replace_parameter(layer, self.i_zp_name,
torch.nn.Parameter(azp, requires_grad=False))
else:
setattr(layer, self.i_s_name, None)
setattr(layer, self.i_zp_name, None)
# Different from cutlass, oneDNN kernels only need the AZP adjustment
# term for dynamic quantization. And s_b should be folded into the
# term. Such as:
# s_a * s_b * [(A - zp_a)B] + bias =
# s_a * (s_b * AB) - s_a * s_b * zp_a * B + bias =
# s_a * GEMM_output - s_a * zp_a * adj + bias
if not (self.config.input_symmetric
and self.config.is_static_input_scheme):
weight = getattr(layer, self.w_q_name)
weight_scale = getattr(layer, self.w_s_name)
azp_adj = weight.sum(dim=0, keepdim=True, dtype=torch.float32)
azp_adj = azp_adj * weight_scale.squeeze()
setattr(layer, self.azp_adj_name,
torch.nn.Parameter(azp_adj, requires_grad=False))
else:
setattr(layer, self.azp_adj_name, None)
weight = getattr(layer, self.w_q_name)
self.dnnl_handler = ops.create_onednn_scaled_mm(
weight,
getattr(layer, self.w_s_name),
torch.get_default_dtype(),
getattr(layer, self.i_s_name) is None,
not self.config.input_symmetric,
32,
)
# weight is prepacked and maintained by the dnnl_handler,
# release the original weight
setattr(layer, self.w_q_name, None)
del weight
def process_weights_for_sgl(self, layer: torch.nn.Module) -> None:
# WEIGHT
weight = getattr(layer, self.w_q_name)
packed_weight = torch.ops._C.convert_weight_packed(weight)
replace_parameter(
layer, self.w_q_name,
torch.nn.Parameter(packed_weight, requires_grad=False))
if layer.bias is not None:
bias = layer.bias
layer.register_parameter(
"bias_fp32",
torch.nn.Parameter(bias.float().data, requires_grad=False))
# WEIGHT SCALE
# CPU SGL kernels only support per-channel.
# For per-tensor quant, convert to the per-channel case.
weight_scale = getattr(layer, self.w_s_name)
if not self.config.is_channelwise:
weight_scale = convert_to_channelwise(weight_scale,
layer.logical_widths)
replace_parameter(
layer, self.w_s_name,
torch.nn.Parameter(weight_scale.data, requires_grad=False))
setattr(layer, self.i_s_name, None)
setattr(layer, self.i_zp_name, None)
setattr(layer, self.azp_adj_name, None)
def apply_weights(self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return self.linear_method(
layer,
x,
bias,
)
def _apply_weights_onednn(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
w_q, w_s, i_s, i_zp, azp_adj = self._get_weight_params(layer)
# ops.scaled_int8_quant supports both dynamic and static quant:
# * dynamic, i_s is None and x_s computed from x.
# * static, i_s is scalar and x_s is i_s.
x_q, x_s, x_zp = ops.onednn_scaled_int8_quant(
x, i_s, i_zp, self.config.input_symmetric)
m = x.size(0)
n = self.dnnl_handler.n
out = torch.empty((m, n), dtype=x.dtype)
ops.onednn_scaled_mm(self.dnnl_handler, x_q, out, x_s, x_zp, azp_adj,
bias)
return out
def _apply_weights_sgl(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
w_q, w_s, _, _, _ = self._get_weight_params(layer)
return torch.ops._C.int8_scaled_mm_with_quant(
x,
w_q,
w_s,
layer.bias_fp32 if bias is not None else None,
x.dtype,
True,
)
vllm/model_executor/layers/quantization/kernels/scaled_mm/cutlass.py
View file @ 7be5d113
......@@ -25,8 +25,8 @@ class CutlassScaledMMLinearKernel(ScaledMMLinearKernel):
def can_implement(
cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, Optional[str]]:
if (not current_platform.is_cuda() and not current_platform.is_cpu()):
return False, "CutlassScaledMM requires running on CUDA or CPU."
if not current_platform.is_cuda():
return False, "CutlassScaledMM requires running on CUDA."
return True, None
......
vllm/model_executor/layers/utils.py
View file @ 7be5d113
......@@ -142,6 +142,12 @@ direct_register_custom_op(
)
def check_cpu_sgl_kernel(n: int, k: int, dtype: torch.dtype):
return (torch._C._cpu._is_amx_tile_supported()
and (dtype in (torch.bfloat16, torch.int8)) and k % 32 == 0
and n % 16 == 0)
def cpu_unquantized_gemm(layer: torch.nn.Module,
x: torch.Tensor,
weight: torch.Tensor,
......
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