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#pragma once
#include <algorithm>
#include <vector>
#ifdef _OPENMP
#include <omp.h>
#endif
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
// for param_search_greedy
#include "caffe2/operators/fused_rowwise_nbitfake_conversion_ops.h"
#include "caffe2/perfkernels/fused_nbit_rowwise_conversion.h"
namespace caffe2 {
template <
int BIT_RATE,
typename T,
void (*convert)(float* dst, const T* src, size_t N),
bool GREEDY = false>
class FloatToFusedNBitRowwiseQuantizedOp final : public Operator<CPUContext> {
public:
FloatToFusedNBitRowwiseQuantizedOp(const OperatorDef& def, Workspace* ws)
: Operator<CPUContext>(def, ws) {}
~FloatToFusedNBitRowwiseQuantizedOp() override {}
bool RunOnDevice() override {
CAFFE_ENFORCE(internal::is_little_endian(), "Unsupported endianness");
const auto& input = Input(DATA_FLOAT);
CAFFE_ENFORCE_GT(input.dim(), 0, "Input's dimension must be at least 1");
const auto input_rows = input.size_to_dim(input.dim() - 1);
const auto input_columns = input.size(input.dim() - 1);
static_assert(8 % BIT_RATE == 0, "BIT_RATE must divide 8");
constexpr int NUM_ELEM_PER_BYTE = 8 / BIT_RATE;
CAFFE_ENFORCE_EQ(
input.dim(input.dim() - 1) % NUM_ELEM_PER_BYTE,
0,
"FloatToFused" + caffe2::to_string(BIT_RATE) +
"BitRowwiseQuantizedOp only works for the number of "
"columns a multiple of " +
caffe2::to_string(NUM_ELEM_PER_BYTE));
// The "fused" representation stores the scale and bias with the
// row-wise quantized data in one tensor.
// Since we represent the scale and bias in 16-bit float, we'll use the
// last 4 bytes of each row for scale (2 bytes) and bias (2 bytes).
// | ... quantized data ... | scale | bias |
// | number_of_columns | 2B | 2B |
auto output_dimensions = input.sizes().vec();
output_dimensions[input.dim() - 1] = static_cast<std::int64_t>(
(input_columns + NUM_ELEM_PER_BYTE - 1) / NUM_ELEM_PER_BYTE +
2 * sizeof(at::Half));
auto* output = Output(
DATA_FUSED_SCALE_BIAS, output_dimensions, at::dtype<std::uint8_t>());
const auto* input_data = input.template data<T>();
auto* output_data = output->template mutable_data<std::uint8_t>();
if (!GREEDY && std::is_same<T, float>::value) {
// fast path
CAFFE_ENFORCE(
reinterpret_cast<void (*)(float*, const float*, std::size_t)>(
convert) == internal::convertfp32fp32,
"When T == float, convert must be convertfp32fp32");
FloatToFusedNBitRowwiseQuantizedSBHalf(
BIT_RATE,
reinterpret_cast<const float*>(input_data),
input_rows,
input_columns,
output_data);
} else {
const auto output_columns = output->size(output->dim() - 1);
#ifdef _OPENMP
vector<float> tmp_vec(
input_columns * (GREEDY ? omp_get_max_threads() : 1));
#else
vector<float> tmp_vec(input_columns);
#endif
#pragma omp parallel for if (GREEDY)
for (int row = 0; row < input_rows; ++row) {
float* tmp = tmp_vec.data();
#ifdef _OPENMP
if (GREEDY) {
tmp = &tmp_vec[omp_get_thread_num() * input_columns];
}
#endif
convert(tmp, input_data + row * input_columns, input_columns);
std::uint8_t* output_row = output_data + row * output_columns;
at::Half* output_row_scale = reinterpret_cast<at::Half*>(
output_row +
(input_columns + NUM_ELEM_PER_BYTE - 1) / NUM_ELEM_PER_BYTE);
at::Half* output_row_bias = reinterpret_cast<at::Half*>(
output_row +
(input_columns + NUM_ELEM_PER_BYTE - 1) / NUM_ELEM_PER_BYTE +
sizeof(at::Half));
float Xmin = *std::min_element(tmp, tmp + input_columns);
float Xmax = *std::max_element(tmp, tmp + input_columns);
if (GREEDY) {
internal::param_search_greedy(
tmp, input_columns, 200, 0.16, Xmin, Xmax, BIT_RATE);
}
// Round Xmin to fp16 to match with dequantization that will use fp16
// for Xmin.
Xmin = static_cast<at::Half>(Xmin);
const float range = Xmax - Xmin;
// Round scale to fp16 to match with dequantization that will use fp16
// for scale.
// Set scale to 1.0f for the corner case of Xmax == Xmin .
// Any non-zero scale would work because during quantization
// (X - Xmin) / scale will be 0 for all X unless scale is 0.
at::Half scale = range == 0 ? 1.0f : range / ((1 << BIT_RATE) - 1);
float inverse_scale = scale == 0 ? 1.0f : 1.0f / scale;
if (scale == 0 || std::isinf(inverse_scale)) {
// Corner case handling when Xmax == Xmin
// Any scale would work because X - Xmin will be 0 for all X
scale = 1.0f;
inverse_scale = 1.0f;
}
*output_row_scale = scale;
*output_row_bias = Xmin;
for (const auto col : c10::irange(input_columns)) {
float X = tmp[col];
std::uint8_t quantized = std::max(
0,
std::min<int>(
std::lrintf((X - Xmin) * inverse_scale),
(1 << BIT_RATE) - 1));
if (col % NUM_ELEM_PER_BYTE == 0) {
output_row[col / NUM_ELEM_PER_BYTE] = quantized;
} else {
output_row[col / NUM_ELEM_PER_BYTE] |=
(quantized << ((col % NUM_ELEM_PER_BYTE) * BIT_RATE));
}
}
}
} // GREEDY || !std::is_same<T, float>::value
return true;
}
private:
INPUT_TAGS(DATA_FLOAT);
OUTPUT_TAGS(DATA_FUSED_SCALE_BIAS);
};
template <
int BIT_RATE,
typename T,
void (*convert)(T* dst, const float* src, size_t N)>
class FusedNBitRowwiseQuantizedToFloatOp final : public Operator<CPUContext> {
public:
FusedNBitRowwiseQuantizedToFloatOp(const OperatorDef& def, Workspace* ws)
: Operator<CPUContext>(def, ws) {}
~FusedNBitRowwiseQuantizedToFloatOp() override {}
bool RunOnDevice() override {
CAFFE_ENFORCE(internal::is_little_endian(), "Unsupported endianness");
const auto& input = Input(DATA_FUSED_SCALE_BIAS);
CAFFE_ENFORCE_GT(input.dim(), 0, "Input's dimension must be at least 1");
const auto input_rows = input.size_to_dim(input.dim() - 1);
const auto input_columns = input.size(input.dim() - 1);
static_assert(8 % BIT_RATE == 0, "BIT_RATE must divide 8");
constexpr int NUM_ELEM_PER_BYTE = 8 / BIT_RATE;
// The last 4 bytes per row are two fp16 scale and bias.
// The rest of input_columns is the number of values in the original row.
auto output_dimensions = input.sizes().vec();
output_dimensions[input.dim() - 1] =
static_cast<std::int64_t>(input_columns - 2 * sizeof(at::Half)) *
NUM_ELEM_PER_BYTE;
auto* output = Output(DATA_FLOAT, output_dimensions, at::dtype<T>());
const auto output_columns = output->size(output->dim() - 1);
const auto* input_data = input.template data<std::uint8_t>();
T* output_data = output->template mutable_data<T>();
if (std::is_same<T, float>::value) {
// fast path
CAFFE_ENFORCE(
reinterpret_cast<void (*)(float*, const float*, std::size_t)>(
convert) == internal::convertfp32fp32,
"When T == float, convert must be convertfp32fp32");
FusedNBitRowwiseQuantizedSBHalfToFloat(
BIT_RATE,
input_data,
input_rows,
input_columns,
reinterpret_cast<float*>(output_data));
} else {
std::vector<float> tmp(output_columns);
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
for (const auto row : c10::irange(input_rows)) {
const std::uint8_t* input_row = input_data + row * input_columns;
float scale = *reinterpret_cast<const at::Half*>(
input_row +
(output_columns + NUM_ELEM_PER_BYTE - 1) / NUM_ELEM_PER_BYTE);
float bias = *reinterpret_cast<const at::Half*>(
input_row +
(output_columns + NUM_ELEM_PER_BYTE - 1) / NUM_ELEM_PER_BYTE +
sizeof(at::Half));
for (const auto col : c10::irange(output_columns)) {
std::uint8_t quantized = input_row[col / NUM_ELEM_PER_BYTE];
quantized >>= (col % NUM_ELEM_PER_BYTE) * BIT_RATE;
quantized &= (1 << BIT_RATE) - 1;
tmp[col] = scale * quantized + bias;
}
convert(output_data + row * output_columns, tmp.data(), output_columns);
}
}
return true;
}
private:
INPUT_TAGS(DATA_FUSED_SCALE_BIAS);
OUTPUT_TAGS(DATA_FLOAT);
};
} // namespace caffe2
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