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CUDA implementation of fakequant (#20252)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20252 Add CUDA implementation for fakequant op for quantization aware training. Reviewed By: zafartahirov Differential Revision: D15243386 fbshipit-source-id: 37610ab046786ffc69aaec5235e5df8304c353d6
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Jerry Zhang
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#include <ATen/ATen.h>
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#include <ATen/cuda/CUDAApplyUtils.cuh>
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#include <ATen/core/op_registration/op_registration.h>
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#include <cmath>
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/* FakeQuantize Op for PerTensorAffine quantization scheme */
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namespace at { namespace native {
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namespace {
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/* Fake-quantizes the 'inputs' tensor.
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Args:
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X: Forward input tensor.
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scale: scale of per tensor affine quantization
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zero_point: zero_point of per tensor affine quantization
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num_bits: Number of quantization bits.
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quant_delay: Count of global steps for which to delay the quantization.
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See note below.
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iter: The current quantization iteration used for `quant_delay`.
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Returns:
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Quantized tensor (double dtype).
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Notes:
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- quant_delay might be set to non-zero to help weights stabilize in the
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beginning of the training.
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- quantization range [0, 2^bits - 1]
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*/
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class FakeQuantizePerTensorAffineOp_forward : public c10::OperatorKernel {
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public:
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at::Tensor operator()(
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at::Tensor X,
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double scale,
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int64_t zero_point,
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int64_t num_bits = 8,
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int64_t quant_delay = 0,
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int64_t iter = 0
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) {
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// Sanity checks.
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TORCH_CHECK(X.is_cuda());
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TORCH_CHECK(X.scalar_type() == ScalarType::Float);
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if (num_bits > 32 || num_bits < 1) {
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throw std::invalid_argument("`num_bits` should be in the [1, 32] range.");
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}
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if (zero_point < 0) {
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throw std::invalid_argument("`zero_point` must be a positive integer.");
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}
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if (quant_delay < 0) {
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throw std::invalid_argument("`quant_delay` must be a positive integer.");
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}
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if (quant_delay != 0 && iter < 0) {
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throw std::invalid_argument(
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"`iter` must be >=0 for non-zero `quant_delay`");
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}
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auto Y = at::empty_like(X);
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if (quant_delay > 0 && iter <= quant_delay) {
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Y.copy_(X); // We might want to just return the input here.
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return Y;
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}
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float inv_scale = 1.0f / scale;
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const float quant_min = 0;
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const float quant_max = (1 << num_bits) - 1;
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at::cuda::CUDA_tensor_apply2<float, float>(
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X,
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Y,
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[=] __device__ (
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const float& input_val,
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float& result_val) {
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result_val = (fminf(quant_max, fmaxf(quant_min, (std::round(input_val * inv_scale + zero_point)))) - zero_point) * scale;
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});
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return Y;
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}
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};
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/* Backward path to fake-quantize the 'inputs' tensor.
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Args:
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X: Forward input tensor.
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dY: Backward input tensor.
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scale: scale of per tensor affine quantization
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zero_point: zero_point of per tensor affine quantization
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num_bits: Number of quantization bits.
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quant_delay: Count of global steps for which to delay the quantization.
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See note in forward.
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iter: The current quantization iteration used for `quant_delay`.
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Returns:
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Quantized tensor (double dtype).
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Notes:
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- quant_delay might be set to non-zero to help weights stabilize in the
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beginning of the training.
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- quantization range [0, 2^bits - 1]
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*/
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class FakeQuantizePerTensorAffineOp_backward : public c10::OperatorKernel {
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public:
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at::Tensor operator()(
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at::Tensor X,
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at::Tensor dY,
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double scale,
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int64_t zero_point,
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int64_t num_bits = 8,
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int64_t quant_delay = 0,
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int64_t iter = 0) {
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// Sanity checks.
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TORCH_CHECK(X.is_cuda());
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TORCH_CHECK(X.scalar_type() == ScalarType::Float);
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if (num_bits > 32 || num_bits < 1) {
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throw std::invalid_argument("`num_bits` should be in the [1, 32] range.");
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}
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if (zero_point < 0) {
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throw std::invalid_argument("`zero_point` must be a positive integer.");
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}
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if (quant_delay < 0) {
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throw std::invalid_argument("`quant_delay` must be a positive integer.");
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}
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if (X.numel() <= 0) {
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return X;
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}
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if (X.numel() != dY.numel()) {
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throw std::invalid_argument("`X` and `dY` are not the same size");
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}
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if (quant_delay != 0 && iter < 0) {
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throw std::invalid_argument(
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"`iter` must be >=0 for non-zero `quant_delay`");
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}
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auto dX = at::zeros_like(dY);
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if (quant_delay > 0 && iter <= quant_delay) {
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dX.copy_(dY);
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return dX;
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}
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float inv_scale = 1.0f / scale;
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const float quant_min = 0;
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const float quant_max = (1 << num_bits) - 1;
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auto mask = at::empty_like(dY);
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at::cuda::CUDA_tensor_apply2<float, float>(
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X,
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mask,
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[=] __device__ (
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const float& input_val,
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float& result_val) {
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float Xq = std::round(input_val * inv_scale + zero_point);
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result_val = float(Xq >= quant_min && Xq <= quant_max);
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});
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dX = mask * dY;
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return dX;
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}
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};
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static auto registry =
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c10::RegisterOperators()
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.op("quantized::fake_quantize_per_tensor_affine_forward(Tensor X, float scale, int zero_point, int num_bits = 8, int quant_delay = 0, int iter = 0) -> Tensor",
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c10::RegisterOperators::options()
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.kernel<FakeQuantizePerTensorAffineOp_forward>()
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.dispatchKey(CUDATensorId()))
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.op("quantized::fake_quantize_per_tensor_affine_backward(Tensor X, Tensor dY, float scale, int zero_point, int num_bits=8, int quant_delay=0, int iter = 0) -> Tensor",
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c10::RegisterOperators::options()
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.kernel<FakeQuantizePerTensorAffineOp_backward>()
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.dispatchKey(CUDATensorId()));
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} // namespace
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}} // namespace at::native
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@ -1,6 +1,7 @@
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from __future__ import absolute_import, division, print_function, unicode_literals
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import torch
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import torch.cuda
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import torch.jit
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import numpy as np
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import unittest
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@ -66,6 +67,9 @@ class TestFakeQuantizePerTensorAffine(unittest.TestCase):
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np.testing.assert_allclose(dX, dX_prime, rtol=tolerance, atol=tolerance)
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def test_numerical_consistency(self):
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'''
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Comparing numerical consistency between CPU quantize/dequantize op and the CPU fake quantize op
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'''
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np.random.seed(NP_RANDOM_SEED)
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fake_quantize_per_tensor_affine_forward = torch.ops.quantized.fake_quantize_per_tensor_affine_forward
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@ -74,13 +78,72 @@ class TestFakeQuantizePerTensorAffine(unittest.TestCase):
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num_bits = 8
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X = np.random.rand(20, 20) * 125
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X_torch = torch.from_numpy(X).float()
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Y = X_torch.quantize_linear(scale, zero_point, torch.qint8).dequantize()
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Y = torch.dequantize(torch.quantize_linear(X_torch, scale, zero_point, torch.qint8))
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Y_prime = fake_quantize_per_tensor_affine_forward(
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X=X_torch, scale=scale, zero_point=zero_point, num_bits=num_bits,
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quant_delay=0, iter=0)
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tolerance = 1e-6
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np.testing.assert_allclose(Y, Y_prime, rtol=tolerance, atol=tolerance)
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"""Tests the forward path of the FakeQuantizePerTensorAffine CUDA op."""
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@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
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def test_forward_cuda(self):
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np.random.seed(NP_RANDOM_SEED)
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fake_quantize_per_tensor_affine_forward = torch.ops.quantized.fake_quantize_per_tensor_affine_forward
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scale = 3
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zero_point = 2
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num_bits = 8
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X = np.random.rand(20, 20) * 125
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X_torch = torch.from_numpy(X).float().cuda()
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Y = _fake_quantize_per_tensor_affine_reference(X, scale, zero_point, num_bits)
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Y_prime = fake_quantize_per_tensor_affine_forward(
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X=X_torch, scale=scale, zero_point=zero_point, num_bits=num_bits,
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quant_delay=0, iter=0)
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tolerance = 1e-6
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np.testing.assert_allclose(Y, Y_prime.cpu(), rtol=tolerance, atol=tolerance)
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"""Tests the backward method. Note that this runs the reference quantization
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and thus the errors might be originating there."""
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@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
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def test_backward_cuda(self):
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np.random.seed(NP_RANDOM_SEED)
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fake_quantize_per_tensor_affine_backward = torch.ops.quantized.fake_quantize_per_tensor_affine_backward
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scale = 3
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zero_point = 2
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num_bits = 8
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X = np.random.rand(20, 20) * 125
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Y = _fake_quantize_per_tensor_affine_reference(X, scale, zero_point, num_bits)
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dY = Y - X # Fake gradient
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dX = _fake_quantize_per_tensor_affine_grad_reference(X, dY, scale, zero_point, num_bits)
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X_torch = torch.from_numpy(X).float().cuda()
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dY_torch = torch.from_numpy(dY).float().cuda()
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dX_prime = fake_quantize_per_tensor_affine_backward(
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X=X_torch, dY=dY_torch, scale=scale, zero_point=zero_point,
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num_bits=num_bits, quant_delay=0, iter=0)
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tolerance = 1e-6
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np.testing.assert_allclose(dX, dX_prime.cpu(), rtol=tolerance, atol=tolerance)
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@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
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def test_numerical_consistency_cuda(self):
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'''
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Comparing numerical consistency between CPU quantize/dequantize op and the CUDA fake quantize op
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'''
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np.random.seed(NP_RANDOM_SEED)
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fake_quantize_per_tensor_affine_forward = torch.ops.quantized.fake_quantize_per_tensor_affine_forward
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scale = 3
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zero_point = 2
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num_bits = 8
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X = np.random.rand(20, 20) * 125
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X_torch = torch.from_numpy(X).float()
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Y = torch.dequantize(torch.quantize_linear(X_torch, scale, zero_point, torch.qint8))
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Y_prime = fake_quantize_per_tensor_affine_forward(
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X=X_torch.cuda(), scale=scale, zero_point=zero_point, num_bits=num_bits,
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quant_delay=0, iter=0)
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tolerance = 1e-6
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np.testing.assert_allclose(Y, Y_prime.cpu(), rtol=tolerance, atol=tolerance)
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if __name__ == '__main__':
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run_tests()
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