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[hgemm/bugfix] Added conditions for 1x8 and 1x4 kernel calls to enhance accuracy @open sesame 05/09 09:04 #2573

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May 13, 2024
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[hgemm/bugfix] Added conditions for 1x8 and 1x4 kernel calls to enhance accuracy @open sesame 05/09 09:04 #2573

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bac6ed0
[hgemm] Interchanged hgemm_noTrans_1x8 and hgemm_noTrans_4x4 calls
s-debadri May 8, 2024
ba95483
[hgemm] Added K divisible condition for 1x8 and 1x4 kernels
s-debadri May 8, 2024
dd91068
[hgemm] Optimizing dimension checks using bitmask
s-debadri May 9, 2024
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28 changes: 16 additions & 12 deletions nntrainer/tensor/hgemm/hgemm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -32,15 +32,17 @@
void hgemm_noTrans(const __fp16 *A, const __fp16 *B, float *C32, unsigned int M,
unsigned int N, unsigned int K, float alpha, float beta) {
if (alpha == 1.F && beta == 0.F) {
if (M % 8 == 0 && N % 16 == 0 && K % 8 == 0) {
// used bitwise operator instead of modulo for performance
// e.g (M % 8) is same as (M & 0x7) which will extract last 3 bits of M
if ((M & 0x7) == 0 && (N & 0xF) == 0 && (K & 0x7) == 0) {
hgemm_noTrans_8x16(M, N, K, A, K, B, N, C32, N, alpha, beta);
} else if (M % 8 == 0 && N % 8 == 0 && K % 8 == 0) {
} else if ((M & 0x7) == 0 && (N & 0x7) == 0 && (K & 0x7) == 0) {
hgemm_noTrans_8x8(M, N, K, A, K, B, N, C32, N, alpha, beta);
} else if (M % 4 == 0 && N % 8 == 0 && K % 4 == 0) {
} else if ((M & 0x3) == 0 && (N & 0x7) == 0 && (K & 0x3) == 0) {
hgemm_noTrans_4x8(M, N, K, A, K, B, N, C32, N, alpha, beta);
} else if (N % 8 == 0) {
} else if ((K & 0x7) == 0 && (N & 0x7) == 0) {
hgemm_noTrans_1x8(M, N, K, A, K, B, N, C32, N, alpha, beta);
} else if (N % 4 == 0) {
} else if ((K & 0x7) == 0 && (N & 0x3) == 0) {
hgemm_noTrans_1x4(M, N, K, A, K, B, N, C32, N, alpha, beta);
} else {
hgemm_noTrans_fallback(M, N, K, A, K, B, N, C32, N, alpha, beta);
Expand All @@ -52,17 +54,19 @@ void hgemm_noTrans(const __fp16 *A, const __fp16 *B, float *C32, unsigned int M,
void hgemm_noTrans(const __fp16 *A, const __fp16 *B, __fp16 *C, unsigned int M,
unsigned int N, unsigned int K, float alpha, float beta) {
if (alpha == 1.F && beta == 0.F) {
if (M % 8 == 0 && N % 16 == 0 && K % 8 == 0) {
// used bitwise operator instead of modulo for performance
// e.g (M % 8) is same as (M & 0x7) which will extract last 3 bits of M
if ((M & 0x7) == 0 && (N & 0xF) == 0 && (K & 0x7) == 0) {
hgemm_noTrans_8x16(M, N, K, A, K, B, N, C, N, alpha, beta);
} else if (M % 8 == 0 && N % 8 == 0 && K % 8 == 0) {
} else if ((M & 0x7) == 0 && (N & 0x7) == 0 && (K & 0x7) == 0) {
hgemm_noTrans_8x8(M, N, K, A, K, B, N, C, N, alpha, beta);
} else if (M % 4 == 0 && N % 8 == 0 && K % 4 == 0) {
} else if ((M & 0x3) == 0 && (N & 0x7) == 0 && (K & 0x3) == 0) {
hgemm_noTrans_4x8(M, N, K, A, K, B, N, C, N, alpha, beta);
} else if (N % 8 == 0) {
hgemm_noTrans_1x8(M, N, K, A, K, B, N, C, N, alpha, beta);
} else if (M % 4 == 0 && N % 4 == 0 && K % 4 == 0) {
} else if ((M & 0x3) == 0 && (N & 0x3) == 0 && (K & 0x3) == 0) {
hgemm_noTrans_4x4(M, N, K, A, K, B, N, C, N, alpha, beta);
} else if (N % 4 == 0) {
} else if ((K & 0x7) == 0 && (N & 0x7) == 0) {
hgemm_noTrans_1x8(M, N, K, A, K, B, N, C, N, alpha, beta);
} else if ((K & 0x7) == 0 && (N & 0x3) == 0) {
hgemm_noTrans_1x4(M, N, K, A, K, B, N, C, N, alpha, beta);
}
}
Expand Down
122 changes: 122 additions & 0 deletions test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -701,6 +701,128 @@ TEST(nntrainer_Tensor, dot_gemm_50_768_20000) {
EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
}

TEST(nntrainer_Tensor, dot_gemm_512_520_1032) {
/// @note GEMM : A X B = C
int batch = 1;
int channel = 1;
int height = 512;
int width = 520;

int height_b = 520;
int width_b = 1032;

bool transA = false;
bool transB = false;

nntrainer::TensorDim::TensorType t_type_nchw_fp16 = {
nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16};

nntrainer::TensorDim::TensorType t_type_nchw_fp32 = {
nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP32};

nntrainer::Tensor A(batch, channel, height, width, t_type_nchw_fp16);
nntrainer::Tensor B(batch, channel, height_b, width_b, t_type_nchw_fp16);

nntrainer::Tensor A_fp32(batch, channel, height, width, t_type_nchw_fp32);
nntrainer::Tensor B_fp32(batch, channel, height_b, width_b, t_type_nchw_fp32);

const float alpha = 1e-1;
const int MOD = 10;

GEN_TEST_INPUT(A, ((i * (batch * height * channel) + j * (batch * height) +
k * (width) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT_B(B, ((i * (batch * height_b * channel) +
j * (batch * height_b) + k * (width_b) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT(A_fp32, ((i * (batch * height * channel) +
j * (batch * height) + k * (width) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT_B(B_fp32, ((i * (batch * height_b * channel) +
j * (batch * height_b) + k * (width_b) + l + 1) %
MOD) *
alpha);

nntrainer::Tensor C = A.dot(B, transA, transB);

nntrainer::Tensor C_fp32 = A_fp32.dot(B_fp32, transA, transB);

float mseErrorNeon =
mse<__fp16>(C.getData<__fp16>(), C_fp32.getData<float>(), C.size());

double cosSimNeon = cosine_similarity<__fp16>(
C.getData<__fp16>(), C_fp32.getData<float>(), C.size());

const float epsilon = 1e-3 * width;

EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
}

TEST(nntrainer_Tensor, dot_gemm_1001_1024_20000) {
/// @note GEMM : A X B = C
int batch = 1;
int channel = 1;
int height = 1001;
int width = 1024;

int height_b = 1024;
int width_b = 20000;

bool transA = false;
bool transB = false;

nntrainer::TensorDim::TensorType t_type_nchw_fp16 = {
nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16};

nntrainer::TensorDim::TensorType t_type_nchw_fp32 = {
nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP32};

nntrainer::Tensor A(batch, channel, height, width, t_type_nchw_fp16);
nntrainer::Tensor B(batch, channel, height_b, width_b, t_type_nchw_fp16);

nntrainer::Tensor A_fp32(batch, channel, height, width, t_type_nchw_fp32);
nntrainer::Tensor B_fp32(batch, channel, height_b, width_b, t_type_nchw_fp32);

const float alpha = 1e-1;
const int MOD = 10;

GEN_TEST_INPUT(A, ((i * (batch * height * channel) + j * (batch * height) +
k * (width) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT_B(B, ((i * (batch * height_b * channel) +
j * (batch * height_b) + k * (width_b) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT(A_fp32, ((i * (batch * height * channel) +
j * (batch * height) + k * (width) + l + 1) %
MOD) *
alpha);
GEN_TEST_INPUT_B(B_fp32, ((i * (batch * height_b * channel) +
j * (batch * height_b) + k * (width_b) + l + 1) %
MOD) *
alpha);

nntrainer::Tensor C = A.dot(B, transA, transB);

nntrainer::Tensor C_fp32 = A_fp32.dot(B_fp32, transA, transB);

float mseErrorNeon =
mse<__fp16>(C.getData<__fp16>(), C_fp32.getData<float>(), C.size());

double cosSimNeon = cosine_similarity<__fp16>(
C.getData<__fp16>(), C_fp32.getData<float>(), C.size());

const float epsilon = 1e-3 * width;

EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
}

TEST(nntrainer_Tensor, dot_gemm_50_768_516) {
/// @note GEMM : A X B = C
int batch = 1;
Expand Down