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FakeQuantPerTensorAffine.cpp
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FakeQuantPerTensorAffine.cpp
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#include <ATen/ATen.h>
#include <ATen/Dispatch.h>
#include <ATen/NativeFunctions.h>
#include <ATen/native/TensorIterator.h>
#include <ATen/native/cpu/Loops.h>
#include <ATen/native/quantized/FakeQuantAffine.h>
// FakeQuantize Op for PerTensorAffine quantization scheme.
namespace at {
namespace native {
// Use REGISTER_DISPATCH to run CPU and CUDA backend.
DEFINE_DISPATCH(fake_quant_tensor_cachemask_stub);
DEFINE_DISPATCH(fake_quant_grad_learnable_tensor_stub);
DEFINE_DISPATCH(fake_quant_tensor_cachemask_tensor_qparams_stub);
/* Fake-quantizes the 'inputs' tensor.
Args:
self: Forward input tensor.
dY: Backward input tensor (_backward op only).
scale: scale of per tensor affine quantization
zero_point: zero_point of per tensor affine quantization
quant_min: minimum quantized value
quant_max: maximum quantized value
Returns:
Quantized tensor (double dtype).
*/
Tensor fake_quantize_per_tensor_affine(
const Tensor& self,
double scale,
int64_t zero_point,
int64_t quant_min,
int64_t quant_max) {
const auto res = at::fake_quantize_per_tensor_affine_cachemask(
self, scale, zero_point, quant_min, quant_max);
return std::get<0>(res);
}
Tensor fake_quantize_per_tensor_affine(
const Tensor& self,
const Tensor& scale,
const Tensor& zero_point,
int64_t quant_min,
int64_t quant_max) {
const auto res = at::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(
self, scale, zero_point, at::ones(1, self.options().dtype(at::kLong)), quant_min, quant_max);
return std::get<0>(res);
}
/* Fake-quantizes the 'inputs' tensor, saving a mask for the backward pass.
This is numerically equivalent to `fake_quantize_per_tensor_affine`,
but has a lower memory overhead in the backward pass.
Args:
self: Forward input tensor.
scale: scale of per tensor affine quantization
zero_point: zero_point of per tensor affine quantization
quant_min: minimum quantized value
quant_max: maximum quantized value
Returns:
Quantized tensor (double dtype).
Mask (bool dtype).
*/
std::tuple<Tensor, Tensor> fake_quantize_per_tensor_affine_cachemask(
const Tensor& self,
double scale,
int64_t zero_point,
int64_t quant_min,
int64_t quant_max) {
TORCH_CHECK(
quant_min <= quant_max,
"`quant_min` should be less than or \
equal to `quant_max`.");
TORCH_CHECK(
zero_point >= quant_min && zero_point <= quant_max,
"`zero_point` must be between `quant_min` and `quant_max`.");
auto Y = at::empty_like(self, self.options(), MemoryFormat::Preserve);
auto mask = at::empty_like(self, at::kBool, MemoryFormat::Preserve);
fake_quant_tensor_cachemask_stub(
self.device().type(), Y, mask, self, scale, zero_point, quant_min, quant_max);
// TODO(future, optional): look into packing the mask further (BoolTensor uses
// 1 byte per element, we only need 1 bit per element).
return std::make_tuple(Y, mask);
}
std::tuple<Tensor, Tensor> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(
const Tensor& self,
const Tensor& scale,
const Tensor& zero_point,
const Tensor& fake_quant_enabled,
int64_t quant_min,
int64_t quant_max) {
TORCH_CHECK(
quant_min <= quant_max,
"`quant_min` should be less than or \
equal to `quant_max`.");
auto Y = at::empty_like(self, self.options(), MemoryFormat::Preserve);
auto mask = at::empty_like(self, at::kBool, MemoryFormat::Preserve);
fake_quant_tensor_cachemask_tensor_qparams_stub(
self.device().type(), Y, mask, self, scale, zero_point, fake_quant_enabled, quant_min, quant_max);
// TODO(future, optional): look into packing the mask further (BoolTensor uses
// 1 byte per element, we only need 1 bit per element).
return std::make_tuple(Y, mask);
}
/* Backward path to fake-quantize the 'inputs' tensor, with mask.
Args:
dY: output grad.
mask: mask tensor from the forward pass.
Returns:
dX (input grad).
*/
Tensor fake_quantize_per_tensor_affine_cachemask_backward(
const Tensor& dY,
const Tensor& mask) {
TORCH_CHECK(mask.scalar_type() == ScalarType::Bool);
TORCH_CHECK(mask.sym_numel() == dY.sym_numel(),
"`mask` and `dY` are not the same size: ",
"`mask` is size ", mask.sym_numel(), " and `dY` is size ", dY.sym_numel());
if (dY.sym_numel() <= 0) {
return dY;
}
// Note: no additional kernels needed, since mask is pre-computed
// and we can use the existing tensor multiplication kernels.
return dY * mask;
}
static int64_t _get_zero_point_from_tensor(
const Tensor& zero_point,
int64_t quant_min,
int64_t quant_max,
bool is_forward) {
float zero_point_fp = zero_point[0].item<float>();
zero_point_fp = is_forward ? std::nearbyint(zero_point_fp) : zero_point_fp + 0.5f;
float zero_point_clamped = std::min(std::max(zero_point_fp, static_cast<float>(quant_min)),
static_cast<float>(quant_max));
return static_cast<int64_t>(zero_point_clamped);
}
Tensor _fake_quantize_learnable_per_tensor_affine(
const Tensor& self,
const Tensor& scale,
const Tensor& zero_point,
int64_t quant_min,
int64_t quant_max,
double grad_factor) {
float scale_val = scale[0].item<float>();
int64_t zero_point_val = native::_get_zero_point_from_tensor(zero_point, quant_min, quant_max, true);
return native::fake_quantize_per_tensor_affine(
self, scale_val, zero_point_val, quant_min, quant_max);
}
std::tuple<Tensor, Tensor, Tensor> _fake_quantize_learnable_per_tensor_affine_backward(
const Tensor& dY,
const Tensor& X,
const Tensor& scale,
const Tensor& zero_point,
int64_t quant_min,
int64_t quant_max,
double grad_factor) {
/* The gradients for scale and zero point are calculated as below:
Let Xfq be the fake quantized version of X.
Let Xq be the quantized version of X (clamped at qmin and qmax).
Let Delta and z be the scale and the zero point.
:math:
\frac{d\Delta }{dx} =
\begin{cases}
q_{\min} - z& \text{ if } X_q= q_{\min} \\
q_{\max} - z& \text{ if } X_q= q_{\max} \\
(X_{fq} - X) / \Delta & \text{ else }
\end{cases}
\frac{dz }{dx} =
\begin{cases}
-\Delta& \text{ if } X_q= q_{\min} \text{ or } X_q = q_{\max} \\
0 & \text{ else }
\end{cases}
*/
float scale_val = scale[0].item<float>();
float inv_scale_val = 1.0f / scale_val;
int64_t zero_point_val = native::_get_zero_point_from_tensor(zero_point, quant_min, quant_max, false);
TORCH_CHECK(dY.scalar_type() == ScalarType::Float);
TORCH_CHECK(X.scalar_type() == ScalarType::Float);
TORCH_CHECK(scale.scalar_type() == ScalarType::Float);
TORCH_CHECK(zero_point.scalar_type() == ScalarType::Float);
TORCH_CHECK(X.numel() == dY.numel(), "`X` and `dY` are not the same size");
TORCH_CHECK(
quant_min <= 0 && quant_max >= 0,
"`quant_min` should be less than or \
equal to `quant_max`, and the quantization range should include 0.");
TORCH_CHECK(
zero_point_val >= quant_min && zero_point_val <= quant_max,
"`zero_point` must be between `quant_min` and `quant_max`.");
if (X.numel() <= 0) {
return std::make_tuple(X, scale, zero_point);
}
auto dX = at::empty_like(X, X.options(), MemoryFormat::Preserve);
auto dScale_vec = at::empty_like(X, X.options(), MemoryFormat::Preserve);
auto dZeroPoint_vec = at::empty_like(X, X.options(), MemoryFormat::Preserve);
auto iter = TensorIteratorConfig()
.add_output(dX)
.add_output(dScale_vec)
.add_output(dZeroPoint_vec)
.add_input(X)
.add_input(dY)
.build();
fake_quant_grad_learnable_tensor_stub(
X.device().type(), iter, scale_val, inv_scale_val, zero_point_val, quant_min, quant_max, grad_factor);
// The total sums over the scale and zero point gradient vectors are what will be returned in the end.
auto dScale = dScale_vec.sum().unsqueeze(0).to(scale.device());
auto dZeroPoint = dZeroPoint_vec.sum().unsqueeze(0).to(zero_point.device());
return std::make_tuple(dX, dScale, dZeroPoint);
}
} // namespace native
} // namespace at