[CPU] add support for mamba causal conv1d for qwen3-next (#12309)
This commit is contained in:
Ma Mingfei
@@ -22,6 +22,29 @@ namespace {
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} \
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}()
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#define AT_DISPATCH_BOOL2(BOOL_V1, BOOL_NAME1, BOOL_V2, BOOL_NAME2, ...) \
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[&] { \
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if (BOOL_V1) { \
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constexpr bool BOOL_NAME1 = true; \
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if (BOOL_V2) { \
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constexpr bool BOOL_NAME2 = true; \
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return __VA_ARGS__(); \
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} else { \
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constexpr bool BOOL_NAME2 = false; \
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return __VA_ARGS__(); \
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} \
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} else { \
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constexpr bool BOOL_NAME1 = false; \
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if (BOOL_V2) { \
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constexpr bool BOOL_NAME2 = true; \
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return __VA_ARGS__(); \
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} else { \
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constexpr bool BOOL_NAME2 = false; \
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return __VA_ARGS__(); \
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} \
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} \
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}()
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// dispatch: bfloat16, float16, int8_t, fp8_e4m3
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#define CPU_DISPATCH_PACKED_TYPES(TYPE, ...) \
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[&] { \
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@@ -105,6 +128,8 @@ namespace {
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#define CHECK_EQ(a, b) TORCH_CHECK((a) == (b), "CHECK_EQ(" #a ", " #b ") failed. ", a, " vs ", b)
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#define CHECK_GE(a, b) TORCH_CHECK((a) >= (b), "CHECK_GE(" #a ", " #b ") failed. ", a, " vs ", b)
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// [NB] Parallel Routines
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//
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// * at::parallel_for - applies for most of generic use cases, this will be compiled
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@@ -321,4 +346,10 @@ struct Unroll<1> {
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}
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};
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// conditional data ptr for optional tensor
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template <typename T>
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inline T* conditional_data_ptr(const std::optional<at::Tensor>& opt) {
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return opt.has_value() ? opt.value().data_ptr<T>() : nullptr;
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}
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} // anonymous namespace
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708
sgl-kernel/csrc/cpu/mamba/conv.cpp
Normal file
708
sgl-kernel/csrc/cpu/mamba/conv.cpp
Normal file
@@ -0,0 +1,708 @@
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#include "common.h"
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#include "gemm.h"
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#include "vec.h"
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namespace {
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template <typename scalar_t>
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inline void copy_stub(scalar_t* __restrict__ y, const scalar_t* __restrict__ x, int64_t size) {
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using Vec = at::vec::Vectorized<scalar_t>;
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const bool is_padding = (x == nullptr);
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for (int64_t d = 0; d < size; d += Vec::size()) {
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Vec data_vec = is_padding ? Vec(0.f) : Vec::loadu(x + d);
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data_vec.store(y + d);
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}
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}
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// no remainder
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template <typename scalar_t>
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void inline update_conv_state(
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scalar_t* __restrict__ conv_states,
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const scalar_t* __restrict__ input,
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int64_t width,
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int64_t dim,
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int64_t seqlen,
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bool has_initial_states) {
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// width for `conv_states`
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int64_t width1 = width - 1;
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int64_t w = 0;
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for (; w < width1 - seqlen; ++w) {
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scalar_t* y = conv_states + w * dim;
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const scalar_t* x = has_initial_states ? conv_states + (w + seqlen) * dim : nullptr;
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copy_stub(y, x, dim);
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}
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for (; w < width1; ++w) {
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scalar_t* y = conv_states + w * dim;
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const scalar_t* x = input + (w + seqlen - width1) * dim;
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copy_stub(y, x, dim);
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}
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}
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// A : [M, BLOCK_N]
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// B : [BLOCK_N, K], prepacked as [K/2, BLOCK_N, 2]
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// C : [M, BLOCK_N]
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// bias : [BLOCK_N]
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//
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// lda : leading dimension of `input` and `out`
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//
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template <typename scalar_t, int K, int BLOCK_N, bool has_bias, bool has_silu>
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struct tinygemm_kernel {
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static inline void apply(
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const scalar_t* __restrict__ A,
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const scalar_t* __restrict__ B,
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scalar_t* __restrict__ C,
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const scalar_t* __restrict__ bias,
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const scalar_t* __restrict__ conv_states,
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bool has_initial_state,
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int64_t M,
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int64_t lda,
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bool is_first_token) {
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TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
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}
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};
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#if defined(CPU_CAPABILITY_AVX512)
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template <int K, int BLOCK_N, bool has_bias, bool has_silu>
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struct tinygemm_kernel<at::BFloat16, K, BLOCK_N, has_bias, has_silu> {
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static inline void apply(
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const at::BFloat16* __restrict__ A,
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const at::BFloat16* __restrict__ B,
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at::BFloat16* __restrict__ C,
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const at::BFloat16* __restrict__ bias,
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const at::BFloat16* __restrict__ conv_states,
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bool has_initial_state,
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int64_t M,
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int64_t lda,
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bool is_first_token) {
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assert(K == 4);
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constexpr int ROWS = K;
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constexpr int COLS = BLOCK_N / block_size_n();
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// leading dimension size for b for next block [K/2, 32, 2]
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constexpr int ldb = block_size_n() * K;
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__m512bh va[ROWS * COLS];
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__m512bh vb[ROWS * COLS];
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__m512 vc[COLS * 2];
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// k: {-3, -2, -1} -> {0, 1, 2}
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auto set_conv_states = [&](int k, int col) -> __m512i {
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return has_initial_state ? _mm512_loadu_si512(conv_states + (k + K - 1) * lda + col * 32)
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: _mm512_setzero_si512();
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};
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#define MM512_LOAD_A(idx) \
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((idx) < 0 && is_first_token) ? (__m512bh)(set_conv_states((idx), col)) \
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: (__m512bh)(_mm512_loadu_si512(A + (idx) * lda + col * 32))
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#define MM512_PACK_A(ap, bp, a, b) \
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do { \
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__m512i r0 = (__m512i)(a); \
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__m512i r1 = (__m512i)(b); \
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__m512i d0 = _mm512_unpacklo_epi16(r0, r1); \
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__m512i d1 = _mm512_unpackhi_epi16(r0, r1); \
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r0 = _mm512_shuffle_i32x4(d0, d1, 0x88); \
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r1 = _mm512_shuffle_i32x4(d0, d1, 0xdd); \
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(ap) = (__m512bh)_mm512_shuffle_i32x4(r0, r1, 0x88); \
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(bp) = (__m512bh)_mm512_shuffle_i32x4(r0, r1, 0xdd); \
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} while (0)
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// step 0 : preload a at time step [-3][-2][-1]
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auto preloada = [&](auto i) {
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constexpr int col = i;
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int64_t m = 0;
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va[1 * COLS + col] = MM512_LOAD_A(m - 3);
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va[2 * COLS + col] = MM512_LOAD_A(m - 2);
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va[3 * COLS + col] = MM512_LOAD_A(m - 1);
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};
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Unroll<COLS>{}(preloada);
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auto loada = [&](auto i, int64_t m) {
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constexpr int col = i;
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// update previous time step
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va[0 * COLS + col] = va[1 * COLS + col];
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va[1 * COLS + col] = va[2 * COLS + col];
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va[2 * COLS + col] = va[3 * COLS + col];
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// load current time step
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va[3 * COLS + col] = MM512_LOAD_A(m);
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};
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// step 1 : load weight for just once
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auto loadb = [&](auto i) {
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constexpr int row = i / COLS;
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constexpr int col = i % COLS;
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vb[row * COLS + col] = (__m512bh)(_mm512_loadu_si512(B + col * ldb + row * 32));
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};
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Unroll<ROWS * COLS>{}(loadb);
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// [NB] accumulates 4x32 bfloat16 blocks
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//
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// +------------+------------+
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// | col0 | col1 |
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// +------------+------------+
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// | va0 va1 | va0 va1 |
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// | va2 va3 | va2 va3 |
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// +------------+------------+
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// | vc0 vc1 | vc0 vc1 |
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// +------------+------------+
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//
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// * va and vb shares the same memory layout
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// * block_n 32 with 4 rows equals to 4 registers
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// * 37 uops with avx512bf16 v.s. 57 uops with avx512f
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//
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auto compute = [&](auto i) {
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constexpr int col = i;
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// init accumulators
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if constexpr (has_bias) {
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__m512i b16 = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(bias + col * 32));
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vc[col * 2 + 0] = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32(b16, 0));
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vc[col * 2 + 1] = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32(b16, 1));
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} else {
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vc[col * 2 + 0] = _mm512_set1_ps(0.f);
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vc[col * 2 + 1] = _mm512_set1_ps(0.f);
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}
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// convert to vnni2 format
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__m512bh va0, va1, va2, va3;
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MM512_PACK_A(va0, va1, va[0 * COLS + col], va[1 * COLS + col]);
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MM512_PACK_A(va2, va3, va[2 * COLS + col], va[3 * COLS + col]);
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// accumulate
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vc[col * 2 + 0] = _mm512_dpbf16_ps(vc[col * 2 + 0], va0, vb[0 * COLS + col]);
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vc[col * 2 + 0] = _mm512_dpbf16_ps(vc[col * 2 + 0], va2, vb[2 * COLS + col]);
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vc[col * 2 + 1] = _mm512_dpbf16_ps(vc[col * 2 + 1], va1, vb[1 * COLS + col]);
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vc[col * 2 + 1] = _mm512_dpbf16_ps(vc[col * 2 + 1], va3, vb[3 * COLS + col]);
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};
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using fVec = at::vec::Vectorized<float>;
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using bVec = at::vec::Vectorized<at::BFloat16>;
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const fVec one = fVec(1.f);
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auto storec = [&](auto i, int64_t m) {
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constexpr int col = i;
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fVec x0 = fVec(vc[col * 2 + 0]);
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fVec x1 = fVec(vc[col * 2 + 1]);
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if constexpr (has_silu) {
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x0 = x0 / (one + x0.neg().exp_u20());
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x1 = x1 / (one + x1.neg().exp_u20());
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}
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bVec out_vec = convert_from_float_ext<at::BFloat16>(x0, x1);
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out_vec.store(C + m * lda + col * 32);
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};
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for (int64_t m = 0; m < M; ++m) {
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// step 3.a : load a at current time step
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Unroll<COLS>{}(loada, m);
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// step 3.b : accumulate for window size (4)
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Unroll<COLS>{}(compute);
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// step 3.c : store c at current time step
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Unroll<COLS>{}(storec, m);
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}
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}
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};
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#endif
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#define LAUNCH_TINYGEMM_KERNEL(K, NB_SIZE) \
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tinygemm_kernel<scalar_t, K, NB_SIZE, has_bias, has_silu>::apply( \
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input + bs * seqlen * dim + mb_start * dim + nb_start, \
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weight + nb_start * width, \
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out + bs * seqlen * dim + mb_start * dim + nb_start, \
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has_bias ? bias + nb_start : nullptr, \
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has_conv_states ? conv_states + conv_state_index * (K - 1) * dim + nb_start : nullptr, \
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has_initial_states_value, \
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mb_size, \
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dim, \
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mb_start == 0);
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template <typename scalar_t>
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void causal_conv1d_fwd_kernel_impl(
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scalar_t* __restrict__ out,
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const scalar_t* __restrict__ input,
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const scalar_t* __restrict__ weight,
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const scalar_t* __restrict__ bias,
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scalar_t* __restrict__ conv_states,
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const int32_t* __restrict__ conv_indices,
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const bool* __restrict__ has_initial_state,
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bool silu_activation,
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int64_t batch,
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int64_t dim,
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int64_t seqlen,
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int64_t width,
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int64_t num_seq_blocks) {
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// handle 32 x 64 per block
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constexpr int64_t BLOCK_M = block_size_m();
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constexpr int64_t BLOCK_N = block_size_n() * 2;
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const int64_t NB = div_up(dim, BLOCK_N);
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const int64_t num_blocks_per_seq = div_up(seqlen, BLOCK_M);
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const bool has_conv_states = conv_states != nullptr;
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const bool has_conv_indices = conv_indices != nullptr;
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// parallel on [batch, seq, NB]
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AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
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at::parallel_for(0, num_seq_blocks * NB, 0, [&](int64_t begin, int64_t end) {
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int64_t mb{0}, nb{0};
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data_index_init(begin, mb, num_seq_blocks, nb, NB);
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for (int64_t i = begin; i < end; ++i) {
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int64_t bs = mb / num_blocks_per_seq;
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int64_t mb_start = (mb % num_blocks_per_seq) * BLOCK_M;
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int64_t mb_size = std::min(seqlen - mb_start, BLOCK_M);
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int64_t nb_start = nb * BLOCK_N;
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int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
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const bool has_initial_states_value = has_conv_states ? has_initial_state[bs] : false;
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int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
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switch (width << 4 | nb_size >> 4) {
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case 0x42:
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LAUNCH_TINYGEMM_KERNEL(4, 32);
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break;
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case 0x44:
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LAUNCH_TINYGEMM_KERNEL(4, 64);
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break;
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default:
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TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
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}
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// move to the next index
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data_index_step(mb, num_seq_blocks, nb, NB);
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}
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});
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});
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// update conv_states if necessary
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if (has_conv_states) {
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at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
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for (int64_t bs = begin; bs < end; ++bs) {
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update_conv_state(
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conv_states + bs * (width - 1) * dim, input + bs * seqlen * dim, width, dim, seqlen, has_initial_state[bs]);
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}
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});
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}
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}
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#define LAUNCH_TINYGEMM_VARLEN_KERNEL(K, NB_SIZE) \
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tinygemm_kernel<scalar_t, K, NB_SIZE, has_bias, has_silu>::apply( \
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input + batch_offset * dim + mb_start * dim + nb_start, \
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weight + nb_start * width, \
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out + batch_offset * dim + mb_start * dim + nb_start, \
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has_bias ? bias + nb_start : nullptr, \
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nullptr, \
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false, \
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mb_size, \
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dim, \
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mb_start == 0);
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// TODO: add `has_initial_state` support for varlen kernel
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template <typename scalar_t>
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void causal_conv1d_fwd_varlen_kernel_impl(
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scalar_t* __restrict__ out,
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const scalar_t* __restrict__ input,
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const scalar_t* __restrict__ weight,
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const scalar_t* __restrict__ bias,
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scalar_t* __restrict__ conv_states,
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const int32_t* __restrict__ query_start_loc,
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const int32_t* __restrict__ conv_indices,
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const bool* __restrict__ has_initial_state,
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const int32_t* __restrict__ block_indices,
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bool silu_activation,
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int64_t batch,
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int64_t dim,
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int64_t width,
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int64_t num_seq_blocks) {
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// handle 32 x 64 per block
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constexpr int64_t BLOCK_M = block_size_m();
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constexpr int64_t BLOCK_N = block_size_n() * 2;
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const int64_t NB = div_up(dim, BLOCK_N);
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const bool has_conv_states = conv_states != nullptr;
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const bool has_conv_indices = conv_indices != nullptr;
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// parallel on [batch, seq, NB]
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AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
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at::parallel_for(0, num_seq_blocks * NB, 0, [&](int64_t begin, int64_t end) {
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int64_t mb{0}, nb{0};
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data_index_init(begin, mb, num_seq_blocks, nb, NB);
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for (int64_t i = begin; i < end; ++i) {
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int32_t bs = block_indices[mb * 2 + 0];
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int32_t batch_offset = query_start_loc[bs];
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int32_t seqlen = query_start_loc[bs + 1] - query_start_loc[bs];
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int64_t mb_start = block_indices[mb * 2 + 1] * BLOCK_M;
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int64_t mb_size = std::min(seqlen - mb_start, BLOCK_M);
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int64_t nb_start = nb * BLOCK_N;
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int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
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switch (width << 4 | nb_size >> 4) {
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case 0x42:
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LAUNCH_TINYGEMM_VARLEN_KERNEL(4, 32);
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break;
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case 0x44:
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LAUNCH_TINYGEMM_VARLEN_KERNEL(4, 64);
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break;
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default:
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TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
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}
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||||
// move to the next index
|
||||
data_index_step(mb, num_seq_blocks, nb, NB);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
// update conv_states if necessary
|
||||
if (has_conv_states) {
|
||||
at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
|
||||
for (int64_t bs = begin; bs < end; ++bs) {
|
||||
int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
|
||||
int32_t seqlen = query_start_loc[bs + 1] - query_start_loc[bs];
|
||||
int32_t batch_offset = query_start_loc[bs];
|
||||
update_conv_state(
|
||||
conv_states + conv_state_index * (width - 1) * dim,
|
||||
input + batch_offset * dim,
|
||||
width,
|
||||
dim,
|
||||
seqlen,
|
||||
/* has_initial_state */ false);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
template <typename scalar_t>
|
||||
void causal_conv1d_update_kernel_impl(
|
||||
scalar_t* __restrict__ out,
|
||||
const scalar_t* __restrict__ input,
|
||||
scalar_t* __restrict__ conv_states,
|
||||
const scalar_t* __restrict__ weight,
|
||||
const scalar_t* __restrict__ bias,
|
||||
const int32_t* __restrict__ conv_indices,
|
||||
bool silu_activation,
|
||||
int64_t batch,
|
||||
int64_t dim,
|
||||
int64_t seqlen,
|
||||
int64_t width) {
|
||||
// handle 32 x 64 per block
|
||||
constexpr int64_t BLOCK_M = block_size_m();
|
||||
constexpr int64_t BLOCK_N = block_size_n() * 2;
|
||||
const int64_t NB = div_up(dim, BLOCK_N);
|
||||
|
||||
const bool has_conv_states = conv_states != nullptr;
|
||||
const bool has_conv_indices = conv_indices != nullptr;
|
||||
|
||||
// parallel on [batch, NB]
|
||||
AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
|
||||
at::parallel_for(0, batch * NB, 0, [&](int64_t begin, int64_t end) {
|
||||
int64_t bs{0}, nb{0};
|
||||
data_index_init(begin, bs, batch, nb, NB);
|
||||
|
||||
for (int64_t i = begin; i < end; ++i) {
|
||||
int64_t mb_start = 0;
|
||||
int64_t mb_size = 1;
|
||||
int64_t nb_start = nb * BLOCK_N;
|
||||
int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
|
||||
|
||||
const bool has_initial_states_value = true;
|
||||
int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
|
||||
|
||||
switch (width << 4 | nb_size >> 4) {
|
||||
case 0x42:
|
||||
LAUNCH_TINYGEMM_KERNEL(4, 32);
|
||||
break;
|
||||
case 0x44:
|
||||
LAUNCH_TINYGEMM_KERNEL(4, 64);
|
||||
break;
|
||||
default:
|
||||
TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
|
||||
}
|
||||
|
||||
// move to the next index
|
||||
data_index_step(bs, batch, nb, NB);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
#define CONV_STATE_INDEXR(w) conv_states + conv_state_index*(width - 1) * dim + (w) * dim
|
||||
|
||||
// update conv_states
|
||||
at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
|
||||
for (int64_t bs = begin; bs < end; ++bs) {
|
||||
// update old states, range [1, width - 1)
|
||||
int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
|
||||
for (int64_t w = 1; w < width - 1; ++w) {
|
||||
std::memcpy(CONV_STATE_INDEXR(w - 1), CONV_STATE_INDEXR(w), dim * sizeof(scalar_t));
|
||||
}
|
||||
// copy new states
|
||||
std::memcpy(CONV_STATE_INDEXR(width - 2), input + bs * dim, dim * sizeof(scalar_t));
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
// from [dim, width] or [N, K]
|
||||
// to [N/BLOCK_N, K/2, BLOCK_N, 2]
|
||||
at::Tensor causal_conv1d_weight_pack(const at::Tensor& weight) {
|
||||
CHECK_INPUT(weight);
|
||||
|
||||
int64_t dim = weight.size(0);
|
||||
int64_t width = weight.size(1);
|
||||
constexpr int64_t BLOCK_N = block_size_n();
|
||||
TORCH_CHECK(width == 4, "causal_conv1d_weight_pack: support only width of 4");
|
||||
TORCH_CHECK(dim % BLOCK_N == 0, "causal_conv1d_weight_pack: invalid dim size ", dim);
|
||||
|
||||
const int64_t N = dim, K2 = width >> 1;
|
||||
const int64_t NB = div_up(N, BLOCK_N);
|
||||
|
||||
auto packed_weight = at::empty_like(weight);
|
||||
AT_DISPATCH_REDUCED_FLOATING_TYPES(weight.scalar_type(), "causal_conv1d_fwd_kernel_impl", [&] {
|
||||
// cast to float32 as vnni size is 2
|
||||
const float* w_data = reinterpret_cast<float*>(weight.data_ptr<scalar_t>());
|
||||
float* packed_data = reinterpret_cast<float*>(packed_weight.data_ptr<scalar_t>());
|
||||
|
||||
at::parallel_for(0, NB * K2 * BLOCK_N, 0, [&](int64_t begin, int64_t end) {
|
||||
int64_t nb{0}, k2{0}, n{0};
|
||||
data_index_init(begin, nb, NB, k2, K2, n, BLOCK_N);
|
||||
|
||||
// TODO: optimize this if we need to online prepacking.
|
||||
for (int64_t i = begin; i < end; ++i) {
|
||||
packed_data[i] = w_data[nb * BLOCK_N * K2 + n * K2 + k2];
|
||||
|
||||
// move to the next index
|
||||
data_index_step(nb, NB, k2, K2, n, BLOCK_N);
|
||||
}
|
||||
});
|
||||
});
|
||||
return packed_weight;
|
||||
}
|
||||
|
||||
#define CHECK_OPTIONAL_SHAPE_DTYPE(OPT, SIZE, DTYPE) \
|
||||
if (OPT.has_value()) { \
|
||||
const auto tensor = OPT.value(); \
|
||||
CHECK_CONTIGUOUS(tensor); \
|
||||
CHECK_EQ(tensor.size(0), SIZE); \
|
||||
CHECK_EQ(tensor.scalar_type(), DTYPE); \
|
||||
}
|
||||
|
||||
template <int BLOCK_M>
|
||||
int64_t get_block_count(const std::optional<at::Tensor>& offsets, int64_t batch, int64_t seqlen) {
|
||||
if (offsets.has_value()) {
|
||||
const int32_t* offsets_data = offsets.value().data_ptr<int32_t>();
|
||||
int32_t num_seq_blocks = 0;
|
||||
for (int64_t row = 0; row < batch; ++row) {
|
||||
num_seq_blocks += div_up(offsets_data[row + 1] - offsets_data[row], BLOCK_M);
|
||||
}
|
||||
return num_seq_blocks;
|
||||
}
|
||||
return batch * div_up(seqlen, int64_t(BLOCK_M));
|
||||
}
|
||||
|
||||
template <int BLOCK_M>
|
||||
at::Tensor get_block_indices(const std::optional<at::Tensor>& offsets, int64_t num_seq_blocks) {
|
||||
if (!offsets.has_value()) {
|
||||
return at::Tensor();
|
||||
}
|
||||
|
||||
const at::Tensor& offsets_ = offsets.value();
|
||||
at::Tensor indices = at::empty({num_seq_blocks, 2}, offsets_.options());
|
||||
|
||||
int64_t batch = offsets_.size(0) - 1;
|
||||
|
||||
const int32_t* offsets_data = offsets_.data_ptr<int32_t>();
|
||||
int32_t* indices_data = indices.data_ptr<int32_t>();
|
||||
|
||||
int64_t idx = 0;
|
||||
for (int32_t row = 0; row < batch; ++row) {
|
||||
int32_t blocks = div_up(offsets_data[row + 1] - offsets_data[row], BLOCK_M);
|
||||
|
||||
for (int32_t col = 0; col < blocks; ++col) {
|
||||
indices_data[idx * 2 + 0] = row;
|
||||
indices_data[idx * 2 + 1] = col;
|
||||
idx++;
|
||||
}
|
||||
}
|
||||
return indices;
|
||||
}
|
||||
|
||||
// API aligned with GPUs
|
||||
//
|
||||
// x: (batch, dim, seqlen) or (dim, cu_seq_len) for varlen
|
||||
// weight: (dim, width)
|
||||
// bias: (dim,)
|
||||
// query_start_loc: (batch + 1) int32
|
||||
// cache_indices: (batch) int32
|
||||
// has_initial_state: (batch) bool
|
||||
// conv_states: (..., dim, width - 1) itype
|
||||
// activation: either None or "silu" or "swish"
|
||||
// pad_slot_id: int
|
||||
//
|
||||
at::Tensor causal_conv1d_fwd_cpu(
|
||||
const at::Tensor& x,
|
||||
const at::Tensor& weight,
|
||||
const std::optional<at::Tensor>& bias,
|
||||
const std::optional<at::Tensor>& conv_states,
|
||||
const std::optional<at::Tensor>& query_start_loc,
|
||||
const std::optional<at::Tensor>& conv_state_indices,
|
||||
const std::optional<at::Tensor>& has_initial_state,
|
||||
bool silu_activation,
|
||||
int64_t pad_slot_id,
|
||||
bool is_vnni) {
|
||||
RECORD_FUNCTION("sgl-kernel::causal_conv1d_fwd_cpu", std::vector<c10::IValue>({x, weight, bias}));
|
||||
|
||||
CHECK_CONTIGUOUS(weight);
|
||||
auto packed_w = is_vnni ? weight : causal_conv1d_weight_pack(weight);
|
||||
|
||||
const bool is_var_seqlen = query_start_loc.has_value();
|
||||
const int64_t input_ndim = is_var_seqlen ? 2 : 3;
|
||||
TORCH_CHECK(x.dim() == input_ndim, "causal_conv1d_fwd_cpu: expect x to be ", input_ndim, "D tensor.");
|
||||
TORCH_CHECK(x.stride(-2) == 1 && x.stride(-1) == x.size(-2), "causal_conv1d_fwd_cpu: expect x to be transposed.");
|
||||
|
||||
const int64_t batch = is_var_seqlen ? query_start_loc.value().size(0) - 1 : x.size(0);
|
||||
const int64_t dim = x.size(-2);
|
||||
const int64_t seqlen = x.size(-1);
|
||||
const int64_t width = weight.size(-1);
|
||||
|
||||
const auto scalar_type = x.scalar_type();
|
||||
CHECK_EQ(weight.scalar_type(), scalar_type);
|
||||
CHECK_OPTIONAL_SHAPE_DTYPE(bias, dim, scalar_type);
|
||||
CHECK_OPTIONAL_SHAPE_DTYPE(query_start_loc, batch + 1, at::kInt);
|
||||
CHECK_OPTIONAL_SHAPE_DTYPE(conv_state_indices, batch, at::kInt);
|
||||
CHECK_OPTIONAL_SHAPE_DTYPE(has_initial_state, batch, at::kBool);
|
||||
|
||||
if (conv_states.has_value()) {
|
||||
auto& conv_states_val = conv_states.value();
|
||||
int64_t padded_batch = conv_states_val.size(0);
|
||||
CHECK_EQ(conv_states_val.scalar_type(), scalar_type);
|
||||
CHECK_GE(padded_batch, batch);
|
||||
CHECK_EQ(conv_states_val.size(1), dim);
|
||||
CHECK_EQ(conv_states_val.size(2), width - 1);
|
||||
|
||||
// adjust `conv_states` to be contiguous on `dim`
|
||||
// should happen only once
|
||||
if (conv_states_val.stride(-2) != 1) {
|
||||
auto conv_states_copy = conv_states_val.clone();
|
||||
conv_states_val.as_strided_({padded_batch, dim, width - 1}, {(width - 1) * dim, 1, dim});
|
||||
conv_states_val.copy_(conv_states_copy);
|
||||
}
|
||||
}
|
||||
|
||||
// block size for sequence blocks, 32
|
||||
constexpr int64_t BLOCK_M = block_size_m();
|
||||
|
||||
// total number of sequence blocks
|
||||
int64_t num_seq_blocks = get_block_count<BLOCK_M>(query_start_loc, batch, seqlen);
|
||||
|
||||
at::Tensor out = at::empty_like(x);
|
||||
AT_DISPATCH_REDUCED_FLOATING_TYPES(scalar_type, "causal_conv1d_fwd_kernel_impl", [&] {
|
||||
if (is_var_seqlen) {
|
||||
// record seq blocks in Coordinate format, aka [num_seq_blocks, 2]
|
||||
at::Tensor block_indices = get_block_indices<BLOCK_M>(query_start_loc, num_seq_blocks);
|
||||
|
||||
causal_conv1d_fwd_varlen_kernel_impl(
|
||||
out.data_ptr<scalar_t>(),
|
||||
x.data_ptr<scalar_t>(),
|
||||
packed_w.data_ptr<scalar_t>(),
|
||||
conditional_data_ptr<scalar_t>(bias),
|
||||
conditional_data_ptr<scalar_t>(conv_states),
|
||||
conditional_data_ptr<int32_t>(query_start_loc),
|
||||
conditional_data_ptr<int32_t>(conv_state_indices),
|
||||
conditional_data_ptr<bool>(has_initial_state),
|
||||
block_indices.data_ptr<int32_t>(),
|
||||
silu_activation,
|
||||
batch,
|
||||
dim,
|
||||
width,
|
||||
num_seq_blocks);
|
||||
} else {
|
||||
causal_conv1d_fwd_kernel_impl<scalar_t>(
|
||||
out.data_ptr<scalar_t>(),
|
||||
x.data_ptr<scalar_t>(),
|
||||
packed_w.data_ptr<scalar_t>(),
|
||||
conditional_data_ptr<scalar_t>(bias),
|
||||
conditional_data_ptr<scalar_t>(conv_states),
|
||||
conditional_data_ptr<int32_t>(conv_state_indices),
|
||||
conditional_data_ptr<bool>(has_initial_state),
|
||||
silu_activation,
|
||||
batch,
|
||||
dim,
|
||||
seqlen,
|
||||
width,
|
||||
num_seq_blocks);
|
||||
}
|
||||
});
|
||||
return out;
|
||||
}
|
||||
|
||||
// API aligned with GPUs
|
||||
//
|
||||
// x: (batch, dim) or (batch, dim, seqlen)
|
||||
// conv_state: (..., dim, state_len), where state_len >= width - 1
|
||||
// weight: (dim, width)
|
||||
// bias: (dim,)
|
||||
// cache_seqlens: (batch,), dtype int32.
|
||||
// conv_state_indices: (batch,), dtype int32
|
||||
// pad_slot_id: int
|
||||
// out: (batch, dim) or (batch, dim, seqlen)
|
||||
//
|
||||
at::Tensor causal_conv1d_update_cpu(
|
||||
const at::Tensor& x,
|
||||
const at::Tensor& conv_states,
|
||||
const at::Tensor& weight,
|
||||
const std::optional<at::Tensor>& bias,
|
||||
bool silu_activation,
|
||||
const std::optional<at::Tensor>& cache_seqlens,
|
||||
const std::optional<at::Tensor>& conv_state_indices,
|
||||
int64_t pad_slot_id,
|
||||
bool is_vnni) {
|
||||
RECORD_FUNCTION("sgl-kernel::causal_conv1d_update_cpu", std::vector<c10::IValue>({x, weight, bias}));
|
||||
|
||||
CHECK_CONTIGUOUS(x);
|
||||
CHECK_CONTIGUOUS(weight);
|
||||
auto packed_w = is_vnni ? weight : causal_conv1d_weight_pack(weight);
|
||||
|
||||
// TODO: add multi-token prediction support
|
||||
TORCH_CHECK(x.dim() == 2, "causal_conv1d_update_cpu: expect x to be 2D tensor.");
|
||||
TORCH_CHECK(!cache_seqlens.has_value(), "causal_conv1d_update_cpu: don't support cache_seqlens.");
|
||||
|
||||
int64_t batch = x.size(0);
|
||||
int64_t dim = x.size(1);
|
||||
int64_t seqlen = 1;
|
||||
int64_t width = weight.size(-1);
|
||||
|
||||
const auto scalar_type = x.scalar_type();
|
||||
CHECK_EQ(weight.scalar_type(), scalar_type);
|
||||
CHECK_OPTIONAL_SHAPE_DTYPE(bias, dim, scalar_type);
|
||||
CHECK_OPTIONAL_SHAPE_DTYPE(conv_state_indices, batch, at::kInt);
|
||||
|
||||
CHECK_EQ(conv_states.scalar_type(), scalar_type);
|
||||
CHECK_EQ(conv_states.size(1), dim);
|
||||
CHECK_EQ(conv_states.size(2), width - 1);
|
||||
|
||||
// adjust `conv_states` to be contiguous on `dim`
|
||||
if (conv_states.stride(-2) != 1) {
|
||||
int64_t num_cache_lines = conv_states.size(0);
|
||||
auto conv_states_copy = conv_states.clone();
|
||||
conv_states.as_strided_({num_cache_lines, dim, width - 1}, {(width - 1) * dim, 1, dim});
|
||||
conv_states.copy_(conv_states_copy);
|
||||
}
|
||||
|
||||
at::Tensor out = at::empty_like(x);
|
||||
AT_DISPATCH_REDUCED_FLOATING_TYPES(scalar_type, "causal_conv1d_update_kernel_impl", [&] {
|
||||
causal_conv1d_update_kernel_impl<scalar_t>(
|
||||
out.data_ptr<scalar_t>(),
|
||||
x.data_ptr<scalar_t>(),
|
||||
conv_states.data_ptr<scalar_t>(),
|
||||
packed_w.data_ptr<scalar_t>(),
|
||||
conditional_data_ptr<scalar_t>(bias),
|
||||
conditional_data_ptr<int32_t>(conv_state_indices),
|
||||
silu_activation,
|
||||
batch,
|
||||
dim,
|
||||
seqlen,
|
||||
width);
|
||||
});
|
||||
return out;
|
||||
}
|
||||
@@ -229,6 +229,32 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> qkv_proj_with_rope_fused_weight(
|
||||
int64_t kv_lora_rank,
|
||||
int64_t qk_rope_head_dim);
|
||||
|
||||
// mamba causal conv1d
|
||||
at::Tensor causal_conv1d_weight_pack(const at::Tensor& weight);
|
||||
|
||||
at::Tensor causal_conv1d_fwd_cpu(
|
||||
const at::Tensor& x,
|
||||
const at::Tensor& weight,
|
||||
const std::optional<at::Tensor>& bias,
|
||||
const std::optional<at::Tensor>& conv_states,
|
||||
const std::optional<at::Tensor>& query_start_loc,
|
||||
const std::optional<at::Tensor>& cache_indices,
|
||||
const std::optional<at::Tensor>& has_initial_state,
|
||||
bool silu_activation,
|
||||
int64_t pad_slot_id,
|
||||
bool is_vnni);
|
||||
|
||||
at::Tensor causal_conv1d_update_cpu(
|
||||
const at::Tensor& x,
|
||||
const at::Tensor& conv_states,
|
||||
const at::Tensor& weight,
|
||||
const std::optional<at::Tensor>& bias,
|
||||
bool silu_activation,
|
||||
const std::optional<at::Tensor>& cache_seqlens,
|
||||
const std::optional<at::Tensor>& conv_state_indices,
|
||||
int64_t pad_slot_id,
|
||||
bool is_vnni);
|
||||
|
||||
// shared memory init
|
||||
void initialize(int64_t size, int64_t rank);
|
||||
|
||||
@@ -383,6 +409,21 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
|
||||
"w2_scale, int[]? block_size, Tensor? a1_scale, Tensor? a2_scale, bool is_vnni) -> Tensor");
|
||||
m.impl("shared_expert_cpu", torch::kCPU, &shared_expert_cpu);
|
||||
|
||||
// causal conv1d
|
||||
m.def("causal_conv1d_weight_pack(Tensor weight) -> Tensor");
|
||||
m.impl("causal_conv1d_weight_pack", torch::kCPU, &causal_conv1d_weight_pack);
|
||||
|
||||
m.def(
|
||||
"causal_conv1d_fwd_cpu(Tensor x, Tensor weight, Tensor? bias, Tensor? conv_states, Tensor? query_start_loc,"
|
||||
"Tensor? cache_indices, Tensor? has_initial_state, bool silu_activation, int pad_slot_id, bool is_vnni) -> "
|
||||
"Tensor");
|
||||
m.impl("causal_conv1d_fwd_cpu", torch::kCPU, &causal_conv1d_fwd_cpu);
|
||||
|
||||
m.def(
|
||||
"causal_conv1d_update_cpu(Tensor x, Tensor conv_states, Tensor weight, Tensor? bias, bool silu_activation,"
|
||||
"Tensor? cache_seqlens, Tensor? conv_state_indices, int pad_slot_id, bool is_vnni) -> Tensor");
|
||||
m.impl("causal_conv1d_update_cpu", torch::kCPU, &causal_conv1d_update_cpu);
|
||||
|
||||
// all reduce
|
||||
m.def("initialize(int size, int rank) -> ()");
|
||||
m.def("shm_allreduce(Tensor(a!) data, int reduce_op) -> ()");
|
||||
|
||||
327
test/srt/cpu/test_causal_conv1d.py
Normal file
327
test/srt/cpu/test_causal_conv1d.py
Normal file
@@ -0,0 +1,327 @@
|
||||
import unittest
|
||||
from typing import Optional
|
||||
|
||||
import sgl_kernel # noqa: F401
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from utils import parametrize, precision
|
||||
|
||||
from sglang.test.test_utils import CustomTestCase
|
||||
|
||||
causal_conv1d_weight_pack = torch.ops.sgl_kernel.causal_conv1d_weight_pack
|
||||
causal_conv1d_fwd = torch.ops.sgl_kernel.causal_conv1d_fwd_cpu
|
||||
causal_conv1d_update = torch.ops.sgl_kernel.causal_conv1d_update_cpu
|
||||
|
||||
|
||||
torch.manual_seed(1234)
|
||||
|
||||
PAD_SLOT_ID = -1
|
||||
|
||||
|
||||
def causal_conv1d_ref(
|
||||
x: torch.Tensor,
|
||||
weight: torch.Tensor,
|
||||
bias: Optional[torch.Tensor] = None,
|
||||
initial_states: Optional[torch.Tensor] = None,
|
||||
return_final_states: bool = False,
|
||||
final_states_out: Optional[torch.Tensor] = None,
|
||||
activation: Optional[str] = "silu",
|
||||
):
|
||||
"""
|
||||
x: (batch, dim, seqlen)
|
||||
weight: (dim, width)
|
||||
bias: (dim,)
|
||||
initial_states: (batch, dim, width - 1)
|
||||
final_states_out: (batch, dim, width - 1)
|
||||
|
||||
out: (batch, dim, seqlen)
|
||||
"""
|
||||
if activation not in [None, "silu", "swish"]:
|
||||
raise NotImplementedError("activation must be None, silu, or swish")
|
||||
dtype_in = x.dtype
|
||||
x = x.to(weight.dtype)
|
||||
seqlen = x.shape[-1]
|
||||
dim, width = weight.shape
|
||||
if initial_states is None:
|
||||
out = F.conv1d(x, weight.unsqueeze(1), bias, padding=width - 1, groups=dim)
|
||||
else:
|
||||
x = torch.cat([initial_states, x], dim=-1)
|
||||
out = F.conv1d(x, weight.unsqueeze(1), bias, padding=0, groups=dim)
|
||||
out = out[..., :seqlen]
|
||||
if return_final_states:
|
||||
final_states = F.pad(x, (width - 1 - x.shape[-1], 0)).to(
|
||||
dtype_in
|
||||
) # (batch, dim, width - 1)
|
||||
if final_states_out is not None:
|
||||
final_states_out.copy_(final_states)
|
||||
else:
|
||||
final_states_out = final_states
|
||||
out = (out if activation is None else F.silu(out)).to(dtype=dtype_in)
|
||||
return (out, None) if not return_final_states else (out, final_states_out)
|
||||
|
||||
|
||||
def causal_conv1d_update_ref(
|
||||
x, conv_state, weight, bias=None, activation=None, cache_seqlens=None
|
||||
):
|
||||
"""
|
||||
x: (batch, dim) or (batch, dim, seqlen)
|
||||
conv_state: (batch, dim, state_len), where state_len >= width - 1
|
||||
weight: (dim, width)
|
||||
bias: (dim,)
|
||||
cache_seqlens: (batch,), dtype int32.
|
||||
If not None, the conv_state is treated as a circular buffer.
|
||||
The conv_state will be updated by copying x to the
|
||||
conv_state starting at the index
|
||||
@cache_seqlens % state_len before performing the convolution.
|
||||
|
||||
out: (batch, dim) or (batch, dim, seqlen)
|
||||
"""
|
||||
if activation not in [None, "silu", "swish"]:
|
||||
raise NotImplementedError("activation must be None, silu, or swish")
|
||||
|
||||
x = x.unsqueeze(-1)
|
||||
batch, dim, seqlen = x.shape
|
||||
width = weight.shape[1]
|
||||
state_len = conv_state.shape[-1]
|
||||
|
||||
x_new = torch.cat([conv_state, x], dim=-1)
|
||||
conv_state.copy_(x_new[:, :, -state_len:])
|
||||
out = F.conv1d(x_new, weight.unsqueeze(1), bias, padding=0, groups=dim)[
|
||||
:, :, -seqlen:
|
||||
]
|
||||
|
||||
out = out.squeeze(-1)
|
||||
return out if activation is None else F.silu(out)
|
||||
|
||||
|
||||
class TestCausalConv1d(CustomTestCase):
|
||||
activation = "silu"
|
||||
|
||||
@parametrize(
|
||||
batch=[1, 1024],
|
||||
dim=[96, 512],
|
||||
seqlen=[2, 36],
|
||||
width=[4],
|
||||
has_bias=[True, False],
|
||||
has_initial_state=[True, False],
|
||||
)
|
||||
def test_causal_conv1d(
|
||||
self,
|
||||
batch,
|
||||
dim,
|
||||
seqlen,
|
||||
width,
|
||||
has_bias,
|
||||
has_initial_state,
|
||||
dtype=torch.bfloat16,
|
||||
prepack=True,
|
||||
):
|
||||
x = torch.randn(batch, seqlen, dim).to(dtype).transpose_(-1, -2)
|
||||
weight = torch.randn(dim, width).to(dtype)
|
||||
bias = torch.randn(dim).to(dtype) if has_bias else None
|
||||
|
||||
if has_initial_state:
|
||||
initial_states = torch.randn(batch, dim, width - 1, dtype=dtype)
|
||||
has_initial_state_tensor = torch.ones(batch, dtype=torch.bool)
|
||||
else:
|
||||
initial_states = None
|
||||
has_initial_state_tensor = None
|
||||
|
||||
packed_weight = causal_conv1d_weight_pack(weight) if prepack else weight
|
||||
|
||||
out_ref, final_states_ref = causal_conv1d_ref(
|
||||
x,
|
||||
weight,
|
||||
bias,
|
||||
initial_states,
|
||||
return_final_states=has_initial_state,
|
||||
activation=self.activation,
|
||||
)
|
||||
|
||||
out = causal_conv1d_fwd(
|
||||
x,
|
||||
packed_weight,
|
||||
bias,
|
||||
initial_states,
|
||||
None,
|
||||
None,
|
||||
has_initial_state_tensor,
|
||||
self.activation in ["silu"],
|
||||
PAD_SLOT_ID,
|
||||
prepack,
|
||||
)
|
||||
|
||||
atol = rtol = precision[dtype]
|
||||
torch.testing.assert_close(out_ref, out, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(
|
||||
final_states_ref, initial_states, atol=atol, rtol=rtol
|
||||
)
|
||||
|
||||
@parametrize(
|
||||
batch=[11],
|
||||
dim=[96],
|
||||
max_seqlen=[66],
|
||||
width=[4],
|
||||
)
|
||||
def test_causal_conv1d_varlen(
|
||||
self,
|
||||
batch,
|
||||
dim,
|
||||
max_seqlen,
|
||||
width,
|
||||
has_bias=False,
|
||||
dtype=torch.bfloat16,
|
||||
prepack=False,
|
||||
):
|
||||
total_entries = batch + 3
|
||||
|
||||
seqlens = torch.randint(1, max_seqlen, (batch + 1,))
|
||||
seqlens[0] = 0
|
||||
# 1 or 2 must test
|
||||
seqlens[-2] = 2
|
||||
|
||||
query_start_loc = torch.cumsum(seqlens, dim=0).to(torch.int32)
|
||||
|
||||
seqlen = query_start_loc[-1].item()
|
||||
x = torch.randn(seqlen, dim, dtype=dtype).transpose_(-1, -2)
|
||||
weight = torch.randn(dim, width, dtype=dtype)
|
||||
bias = torch.randn(dim, dtype=dtype) if has_bias else None
|
||||
|
||||
final_states = torch.randn(total_entries, dim, width - 1, dtype=dtype)
|
||||
final_states_ref = final_states.clone()
|
||||
|
||||
has_initial_states = torch.randint(0, 2, (batch,), dtype=torch.bool).fill_(
|
||||
False
|
||||
)
|
||||
state_indices = torch.randperm(total_entries, dtype=torch.int32)[:batch]
|
||||
|
||||
out_ref = []
|
||||
out_ref_b = []
|
||||
|
||||
return_final_states = final_states is not None
|
||||
splits = torch.split(x, seqlens[1:].tolist(), dim=1)
|
||||
for i, x_s in enumerate(splits):
|
||||
out_ref_b.append(
|
||||
causal_conv1d_ref(
|
||||
x_s.unsqueeze(0),
|
||||
weight,
|
||||
bias,
|
||||
activation=self.activation,
|
||||
return_final_states=return_final_states,
|
||||
final_states_out=(
|
||||
final_states_ref[state_indices[i]].unsqueeze(0)
|
||||
if return_final_states
|
||||
else None
|
||||
),
|
||||
initial_states=(
|
||||
final_states_ref[state_indices[i]].unsqueeze(0)
|
||||
if has_initial_states[i]
|
||||
else None
|
||||
),
|
||||
)
|
||||
)
|
||||
out_ref.append(torch.cat([t[0] for t in out_ref_b], dim=2))
|
||||
out_ref_tensor = torch.cat(out_ref, dim=0).squeeze(0)
|
||||
|
||||
out = causal_conv1d_fwd(
|
||||
x,
|
||||
weight,
|
||||
bias,
|
||||
final_states,
|
||||
query_start_loc,
|
||||
state_indices,
|
||||
has_initial_states,
|
||||
self.activation in ["silu"],
|
||||
PAD_SLOT_ID,
|
||||
prepack,
|
||||
)
|
||||
|
||||
atol = rtol = precision[dtype]
|
||||
torch.testing.assert_close(out_ref_tensor, out, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(final_states_ref, final_states, atol=atol, rtol=rtol)
|
||||
|
||||
@parametrize(
|
||||
batch=[11],
|
||||
dim=[32, 64, 96],
|
||||
width=[4],
|
||||
)
|
||||
def test_causal_conv1d_update(
|
||||
self, batch, dim, width, has_bias=False, dtype=torch.bfloat16, prepack=True
|
||||
):
|
||||
x = torch.randn(batch, dim).to(dtype)
|
||||
conv_state = torch.randn(batch, dim, width - 1, dtype=dtype)
|
||||
weight = torch.randn(dim, width).to(dtype)
|
||||
bias = torch.randn(dim).to(dtype) if has_bias else None
|
||||
|
||||
packed_weight = causal_conv1d_weight_pack(weight) if prepack else weight
|
||||
|
||||
conv_state_ref = conv_state.clone()
|
||||
out_ref = causal_conv1d_update_ref(
|
||||
x, conv_state_ref, weight, bias, activation=self.activation
|
||||
)
|
||||
|
||||
cache_seqlens = None
|
||||
conv_state_indices = None
|
||||
out = causal_conv1d_update(
|
||||
x,
|
||||
conv_state,
|
||||
packed_weight,
|
||||
bias,
|
||||
self.activation in ["silu"],
|
||||
cache_seqlens,
|
||||
conv_state_indices,
|
||||
PAD_SLOT_ID,
|
||||
prepack,
|
||||
)
|
||||
|
||||
atol = rtol = precision[dtype]
|
||||
torch.testing.assert_close(out_ref, out, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(conv_state_ref, conv_state, atol=atol, rtol=rtol)
|
||||
|
||||
@parametrize(
|
||||
batch=[7],
|
||||
dim=[96],
|
||||
width=[4],
|
||||
)
|
||||
def test_causal_conv1d_update_with_batch_gather(
|
||||
self, batch, dim, width, has_bias=False, dtype=torch.bfloat16, prepack=True
|
||||
):
|
||||
total_entries = batch + 3
|
||||
|
||||
x = torch.randn(batch, dim).to(dtype=dtype)
|
||||
|
||||
conv_state_indices = torch.randperm(total_entries)[:batch].to(dtype=torch.int32)
|
||||
conv_state = torch.randn(total_entries, dim, width - 1, dtype=dtype)
|
||||
|
||||
weight = torch.randn(dim, width).to(dtype=dtype)
|
||||
bias = torch.randn(dim).to(dtype=dtype) if has_bias else None
|
||||
conv_state_ref = conv_state[conv_state_indices, :]
|
||||
|
||||
packed_weight = causal_conv1d_weight_pack(weight) if prepack else weight
|
||||
|
||||
out_ref = causal_conv1d_update_ref(
|
||||
x, conv_state_ref, weight, bias, activation=self.activation
|
||||
)
|
||||
|
||||
cache_seqlens = None
|
||||
out = causal_conv1d_update(
|
||||
x,
|
||||
conv_state,
|
||||
packed_weight,
|
||||
bias,
|
||||
self.activation in ["silu"],
|
||||
cache_seqlens,
|
||||
conv_state_indices,
|
||||
PAD_SLOT_ID,
|
||||
prepack,
|
||||
)
|
||||
|
||||
atol = rtol = precision[dtype]
|
||||
torch.testing.assert_close(out_ref, out, atol=atol, rtol=rtol)
|
||||
torch.testing.assert_close(
|
||||
conv_state_ref, conv_state[conv_state_indices, :], atol=atol, rtol=rtol
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -1,3 +1,4 @@
|
||||
import itertools
|
||||
import math
|
||||
|
||||
import torch
|
||||
@@ -15,6 +16,19 @@ factor_for_scale = 1e-3
|
||||
fp8_max, fp8_min = 400, -400
|
||||
|
||||
|
||||
def parametrize(**params):
|
||||
def decorator(func):
|
||||
def wrapper(self):
|
||||
for combo in itertools.product(*params.values()):
|
||||
kwargs = dict(zip(params.keys(), combo))
|
||||
with self.subTest(**kwargs):
|
||||
func(self, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def SiluAndMul(x: torch.Tensor) -> torch.Tensor:
|
||||
d = x.shape[-1] // 2
|
||||
return F.silu(x[..., :d]) * x[..., d:]
|
||||
|
||||
@@ -323,14 +323,18 @@ suite_amd = {
|
||||
}
|
||||
|
||||
# Add Intel Xeon tests
|
||||
# NOTE: please sort the test cases alphabetically by the test file name
|
||||
suite_xeon = {
|
||||
"per-commit-cpu": [
|
||||
TestFile("cpu/test_activation.py"),
|
||||
TestFile("cpu/test_binding.py"),
|
||||
TestFile("cpu/test_causal_conv1d.py"),
|
||||
TestFile("cpu/test_cpu_graph.py"),
|
||||
TestFile("cpu/test_decode.py"),
|
||||
TestFile("cpu/test_extend.py"),
|
||||
TestFile("cpu/test_gemm.py"),
|
||||
TestFile("cpu/test_intel_amx_attention_backend_a.py"),
|
||||
TestFile("cpu/test_intel_amx_attention_backend_b.py"),
|
||||
TestFile("cpu/test_intel_amx_attention_backend_c.py"),
|
||||
TestFile("cpu/test_mamba.py"),
|
||||
TestFile("cpu/test_mla.py"),
|
||||
TestFile("cpu/test_moe.py"),
|
||||
@@ -340,10 +344,6 @@ suite_xeon = {
|
||||
TestFile("cpu/test_rope.py"),
|
||||
TestFile("cpu/test_shared_expert.py"),
|
||||
TestFile("cpu/test_topk.py"),
|
||||
TestFile("cpu/test_cpu_graph.py"),
|
||||
TestFile("cpu/test_intel_amx_attention_backend_a.py"),
|
||||
TestFile("cpu/test_intel_amx_attention_backend_b.py"),
|
||||
TestFile("cpu/test_intel_amx_attention_backend_c.py"),
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user