frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-20 21:14:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

Revert "[Quant][CPU] fix fake_quantize_per_tensor_affine of inf values (#155109)"

Browse Source 
This reverts commit e375d21bb9b0ef6fefe7a8af5a054a17de8c63c9.

Reverted https://github.com/pytorch/pytorch/pull/155109 on behalf of https://github.com/malfet due to Looks like it broke ROCM tests ([comment](https://github.com/pytorch/pytorch/pull/155109#issuecomment-2977428354))
This commit is contained in:
PyTorch MergeBot
2025-06-16 17:22:53 +00:00
parent 45596ec58f
commit e9fdaf8701
2 changed files with 6 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
aten/src/ATen/native/quantized/cpu/kernels/QuantizedOpKernels.cpp
View File

@ -2699,11 +2699,10 @@ void _fake_quantize_tensor_helper(
bool* mask_val = (bool*)(data[1] + i * strides[1]);
scalar_t* input_val = (scalar_t*)(data[2] + i * strides[2]);
const auto qval = static_cast<int64_t>(z_point + std::nearbyint(*input_val * inv_scale));
if (fake_quant_on) {
auto qval_f = z_point + std::nearbyint(*input_val * inv_scale);
const auto qval = static_cast<int64_t>(std::fmin(std::fmax(qval_f, quant_min), quant_max));
*output_val = (qval - z_point) * sc;
*mask_val = ((quant_min <= qval_f) && (qval_f <= quant_max));
*output_val = (std::fmin(std::fmax(qval, quant_min), quant_max) - z_point) * sc;
*mask_val = ((quant_min <= qval) && (qval <= quant_max));
} else {
*output_val = *input_val;
*mask_val = 1;
@ -2719,11 +2718,10 @@ void _fake_quantize_tensor_helper(
bool* mask_val = (bool*)(data[1] + i * strides[1]);
scalar_t* input_val = (scalar_t*)(data[2] + i * strides[2]);
const auto qval = static_cast<int64_t>(z_point + std::nearbyint(*input_val * inv_scale));
if (fake_quant_on) {
auto qval_f = z_point + std::nearbyint(*input_val * inv_scale);
const auto qval = static_cast<int64_t>(std::fmin(std::fmax(qval_f, quant_min), quant_max));
*output_val = (qval - z_point) * sc;
*mask_val = ((quant_min <= qval_f) && (qval_f <= quant_max));
*output_val = (std::fmin(std::fmax(qval, quant_min), quant_max) - z_point) * sc;
*mask_val = ((quant_min <= qval) && (qval <= quant_max));
} else {
*output_val = *input_val;
*mask_val = 1;

15
test/quantization/core/test_workflow_ops.py
View File

@ -1038,21 +1038,6 @@ class TestFakeQuantizeOps(TestCase):
input, scale, zero_point, axis, quant_min, quant_max
)
@skipIfTorchDynamo("Not a suitable test for TorchDynamo")
@given(dtype=st.sampled_from([torch.float, torch.float64, torch.half, torch.bfloat16]),
device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']))
def test_fake_quantize_per_tensor_affine_inf(self, dtype, device) -> None:
# https://github.com/pytorch/pytorch/issues/154328
input_tensor = torch.tensor([torch.inf], dtype=dtype).to(device)
scale = 0.01
zero_point = 0
quant_min = 0
quant_max = 255
result = torch.fake_quantize_per_tensor_affine(input_tensor, scale, zero_point, quant_min, quant_max)
ref_result = (min(quant_max, max(quant_min, torch.round(input_tensor / scale) + zero_point)) - zero_point) * scale
ref_result = torch.Tensor([ref_result]).to(dtype).to(device)
self.assertEqual(result, ref_result)
class TestFusedObsFakeQuant(TestCase):
@given(device=st.sampled_from(['cpu', 'cuda'] if torch.cuda.is_available() else ['cpu']),