[Feat] dnnl build for AVX2 W8A8 Int8 (#41318)

Signed-off-by: Li, Tianmu <[email protected]>
Co-authored-by: Li, Jiang <[email protected]>
This commit is contained in:
Tianmu Li
2026-05-06 15:28:02 +08:00
committed by GitHub
co-authored by Li, Jiang <[email protected]>
parent 809b98e5b7
commit e87e09a50a
4 changed files with 146 additions and 20 deletions
+10 -11
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@@ -326,14 +326,6 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
set(ONEDNN_VERBOSE "ON")
set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
# TODO: Refactor this
if (ENABLE_X86_ISA)
# Note: only enable oneDNN for AVX512
list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS_AVX512})
else()
list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS})
endif()
set(VLLM_BUILD_TYPE ${CMAKE_BUILD_TYPE})
set(CMAKE_BUILD_TYPE "Release") # remove oneDNN debug symbols to reduce size
FetchContent_MakeAvailable(oneDNN)
@@ -346,8 +338,14 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
PRIVATE ${oneDNN_SOURCE_DIR}/src
)
target_link_libraries(dnnl_ext dnnl torch)
target_compile_options(dnnl_ext PRIVATE ${DNNL_COMPILE_FLAGS} -fPIC)
if (ENABLE_X86_ISA)
target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS_AVX2} -fPIC)
else()
target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS} -fPIC)
endif()
list(APPEND LIBS dnnl_ext)
set(USE_ONEDNN ON)
else()
set(USE_ONEDNN OFF)
@@ -435,10 +433,11 @@ if (ENABLE_X86_ISA)
"csrc/cpu/pos_encoding.cpp"
"csrc/moe/dynamic_4bit_int_moe_cpu.cpp")
set(VLLM_EXT_SRC_AVX2
set(VLLM_EXT_SRC_AVX2
"csrc/cpu/utils.cpp"
"csrc/cpu/spec_decode_utils.cpp"
"csrc/cpu/cpu_attn.cpp"
"csrc/cpu/dnnl_kernels.cpp"
"csrc/cpu/torch_bindings.cpp"
# TODO: Remove these files
"csrc/cpu/activation.cpp"
@@ -453,7 +452,7 @@ if (ENABLE_X86_ISA)
set(_C_LIBS numa dnnl_ext)
set(_C_AVX512_LIBS numa dnnl_ext)
set(_C_AVX2_LIBS numa)
set(_C_AVX2_LIBS numa dnnl_ext)
# AMX + AVX512F + AVX512BF16 + AVX512VNNI
define_extension_target(
+133 -6
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@@ -122,9 +122,17 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
void save(void* ptr, const int elem_num) const {
#ifdef __AVX512BW__
constexpr uint32_t M = 0xFFFFFFFF;
__mmask16 mask = _cvtu32_mask16(M >> (32 - elem_num));
_mm256_mask_storeu_epi16(ptr, mask, reg);
#else
// Fallback for lack of 16-bit masked store
int16_t tmp[VEC_ELEM_NUM];
_mm256_storeu_si256((__m256i*)tmp, reg);
for (int i = 0; i < elem_num; ++i)
reinterpret_cast<int16_t*>(ptr)[i] = tmp[i];
#endif
}
};
@@ -161,9 +169,17 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
void save(void* ptr, const int elem_num) const {
#ifdef __AVX512BW__
constexpr uint32_t M = 0xFFFFFFFF;
__mmask16 mask = _cvtu32_mask16(M >> (32 - elem_num));
_mm256_mask_storeu_epi16(ptr, mask, reg);
#else
// Fallback for lack of 16-bit masked store
int16_t tmp[VEC_ELEM_NUM];
_mm256_storeu_si256((__m256i*)tmp, reg);
for (int i = 0; i < elem_num; ++i)
reinterpret_cast<int16_t*>(ptr)[i] = tmp[i];
#endif
}
};
@@ -247,13 +263,12 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
explicit BF16Vec32(__m256i low, __m256i high)
: reg_low(low), reg_high(high) {}
explicit BF16Vec32()
: reg_low(_mm256_setzero_si256()), reg_high(_mm256_setzero_si256()) {}
explicit BF16Vec32(BF16Vec8& vec8_data)
: reg_low((__m256i)_mm256_inserti32x4(
_mm256_castsi128_si256((__m128i)vec8_data.reg),
(__m128i)vec8_data.reg, 1)),
reg_high((__m256i)_mm256_inserti32x4(
_mm256_castsi128_si256((__m128i)vec8_data.reg),
(__m128i)vec8_data.reg, 1)) {}
: reg_low(_mm256_broadcastsi128_si256((__m128i)vec8_data.reg)),
reg_high(_mm256_broadcastsi128_si256((__m128i)vec8_data.reg)) {}
// E4M3 decode (AVX2 path) — same bit-layout trick as the AVX512 variant
// above. Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
@@ -674,6 +689,11 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
_mm256_sub_ps(reg_high, b.reg_high));
}
FP32Vec16 operator-() const {
const __m256 neg = _mm256_set1_ps(-0.0f);
return FP32Vec16(_mm256_xor_ps(reg_low, neg), _mm256_xor_ps(reg_high, neg));
}
FP32Vec16 operator/(const FP32Vec16& b) const {
return FP32Vec16(_mm256_div_ps(reg_low, b.reg_low),
_mm256_div_ps(reg_high, b.reg_high));
@@ -739,6 +759,85 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
_mm256_storeu_ps(ptr, reg_low);
_mm256_storeu_ps(ptr + 8, reg_high);
}
void save(float* ptr, const int elem_num) const {
// Partial store: cmpgt produces a sign-bit mask (0xFFFFFFFF/0 per lane)
// for the first elem_num lanes, applied across the two 8-wide halves.
if (elem_num <= 8) {
__m256i mask =
_mm256_cmpgt_epi32(_mm256_set1_epi32(elem_num),
_mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7));
_mm256_maskstore_ps(ptr, mask, reg_low);
} else {
_mm256_storeu_ps(ptr, reg_low);
__m256i mask =
_mm256_cmpgt_epi32(_mm256_set1_epi32(elem_num - 8),
_mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7));
_mm256_maskstore_ps(ptr + 8, mask, reg_high);
}
}
FP32Vec16 clamp(const FP32Vec16& min, const FP32Vec16& max) const {
return FP32Vec16(
_mm256_min_ps(max.reg_low, _mm256_max_ps(min.reg_low, reg_low)),
_mm256_min_ps(max.reg_high, _mm256_max_ps(min.reg_high, reg_high)));
}
FP32Vec16 abs() const {
const __m256 sign_mask = _mm256_set1_ps(-0.0f);
return FP32Vec16(_mm256_andnot_ps(sign_mask, reg_low),
_mm256_andnot_ps(sign_mask, reg_high));
}
FP32Vec16 min(const FP32Vec16& b) const {
return FP32Vec16(_mm256_min_ps(reg_low, b.reg_low),
_mm256_min_ps(reg_high, b.reg_high));
}
// Partial element-wise min over the first elem_num lanes only (tail path).
// Scalar via AliasReg: AVX2 has no masked vminps, so we spill, loop, reload.
FP32Vec16 min(const FP32Vec16& b, const int elem_num) const {
AliasReg ar_this_low, ar_this_high, ar_b_low, ar_b_high;
ar_this_low.reg = reg_low;
ar_this_high.reg = reg_high;
ar_b_low.reg = b.reg_low;
ar_b_high.reg = b.reg_high;
for (int i = 0; i < elem_num && i < 8; ++i)
ar_this_low.values[i] =
std::min(ar_this_low.values[i], ar_b_low.values[i]);
for (int i = 0; i < elem_num - 8 && i < 8; ++i)
ar_this_high.values[i] =
std::min(ar_this_high.values[i], ar_b_high.values[i]);
return FP32Vec16(ar_this_low.reg, ar_this_high.reg);
}
// Partial element-wise max over the first elem_num lanes only (tail path).
// Scalar via AliasReg: AVX2 has no masked vmaxps, so we spill, loop, reload.
FP32Vec16 max(const FP32Vec16& b, const int elem_num) const {
AliasReg ar_this_low, ar_this_high, ar_b_low, ar_b_high;
ar_this_low.reg = reg_low;
ar_this_high.reg = reg_high;
ar_b_low.reg = b.reg_low;
ar_b_high.reg = b.reg_high;
for (int i = 0; i < elem_num && i < 8; ++i)
ar_this_low.values[i] =
std::max(ar_this_low.values[i], ar_b_low.values[i]);
for (int i = 0; i < elem_num - 8 && i < 8; ++i)
ar_this_high.values[i] =
std::max(ar_this_high.values[i], ar_b_high.values[i]);
return FP32Vec16(ar_this_low.reg, ar_this_high.reg);
}
float reduce_min() const {
__m256 v = _mm256_min_ps(reg_low, reg_high);
__m256 v_shuffled = _mm256_permute_ps(v, 0b00001011);
__m256 v_min = _mm256_min_ps(v, v_shuffled);
v_shuffled = _mm256_permute_ps(v_min, 0b00000001);
v_min = _mm256_min_ps(v_min, v_shuffled);
v_shuffled = _mm256_permute2f128_ps(v_min, v_min, 0b00000001);
v_min = _mm256_min_ps(v_min, v_shuffled);
return _mm256_cvtss_f32(v_min);
}
};
#endif
@@ -791,6 +890,34 @@ struct INT8Vec64 : public Vec<INT8Vec64> {
// non-temporal save
void nt_save(int8_t* ptr) { _mm512_stream_si512((__m512i*)ptr, reg); }
};
#else
struct INT8Vec16 : public Vec<INT8Vec16> {
constexpr static int VEC_ELEM_NUM = 16;
union AliasReg {
__m128i reg;
int8_t values[VEC_ELEM_NUM];
};
__m128i reg;
explicit INT8Vec16(const FP32Vec16& vec) {
__m256i lo_i32 = _mm256_cvtps_epi32(vec.reg_low);
__m256i hi_i32 = _mm256_cvtps_epi32(vec.reg_high);
__m256i packed16 = _mm256_packs_epi32(lo_i32, hi_i32);
packed16 = _mm256_permute4x64_epi64(packed16, 0xD8);
__m256i packed8 = _mm256_packs_epi16(packed16, _mm256_setzero_si256());
packed8 = _mm256_permute4x64_epi64(packed8, 0xD8);
reg = _mm256_castsi256_si128(packed8);
}
void save(int8_t* ptr) const { _mm_storeu_si128((__m128i*)ptr, reg); }
void save(int8_t* ptr, const int elem_num) const {
AliasReg ar;
ar.reg = reg;
for (int i = 0; i < elem_num; ++i) ptr[i] = ar.values[i];
}
};
#endif
template <typename T>
+1 -1
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@@ -215,7 +215,7 @@ void dynamic_quant_epilogue(const float* input, scalar_t* output,
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) {
for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
cvt_vec_t elems_fp32(input_ptr + j);
elems_fp32 = elems_fp32 * token_scale_vec;
if constexpr (AZP) {
+2 -2
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@@ -278,8 +278,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
// Quantization
#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__)) || \
defined(__powerpc64__)
#if defined(__AVX512F__) || defined(__AVX2__) || \
(defined(__aarch64__) && !defined(__APPLE__)) || defined(__powerpc64__)
// Helper function to release oneDNN handlers
ops.def("release_dnnl_matmul_handler(int handler) -> ()",
&release_dnnl_matmul_handler);