[PT FE] Add aten::quantize_per_tensor, aten::quantize_per_channel, aten::dequantize (#18266)

* Support GetAttr with packed params * Apply suggestions from code review * [PT FE] Add quantized types as normal types to decoder * [PT FE] Add decoder dequantize, add dtypes to quantize * [PT FE] Add dequantize example * [PT FE] Implement replacer for quantized nodes * [PT FE] Register replacer for quantize/dequantize * [PT FE] Remove unwanted junk from previous version * [PT FE] Fix building mistakes for frontend * [PT FE] Clang fix * [PT FE] Ease of use upgrade to quantize funcs * [PT FE] Clang format * [PT FE] Introduce new version of quantize/dequantize * [PT FE] Remove unwanted files from new version * [PT FE] Fix style * [PT FE] Add QuantizedPtNode replacer, fix accuracy error * [PT FE] Add improved version of quantize/dequantize with shared_ptrs * [PT FE] Fix utils shared ptr reference error * [PT FE] Quantize now takes correct input for operations * [PT FE] Upgrade quantize method * [PT FE] Add BFS for dequantize, add quantize_per_channel * [PT FE] Add missing replacer to frontend, improve tests * [PT FE] Rename replacer -> remover, remove unwanted header files * [PT FE] Change function declarations to return ov::Output instead of shared ptr * [PT FE] Add missing context mark node * [PT FE] Remove unknown modifications to ie_c_api * [PT FE] Remove fp16 support, turn off int32 tests * [PT FE] Clang format * [PT FE] Fix quantize_per_tensor * [PT FE] Minor fixes from review * [PT FE] Remove dequantize, remove helpers, replacer now removes nodes instead * [PT FE] Rename Replacer to Remover for dequantize nodes * [PT FE] Clang format * [PT FE] Move comments to header files, minor import fixes * [PT FE] Fix clang format * [PT FE] Fix dtype issue * [PT FE] Fix quantize_per_channel tests * Apply suggestions from code review Removing sporadic tests from precommit * Apply suggestions from code review --------- Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
2023-07-17 02:30:02 +02:00
parent 8defcef3f1
commit d7984f3ec2
12 changed files with 587 additions and 9 deletions
--- a/tests/layer_tests/pytorch_tests/test_quantize.py
+++ b/tests/layer_tests/pytorch_tests/test_quantize.py
@@ -0,0 +1,87 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import torch
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+class aten_quantize_per_tensor_aten_dequantize(torch.nn.Module):
+    def __init__(self, scale, zero_point, dtype) -> None:
+        torch.nn.Module.__init__(self)
+        self.scale = scale
+        self.zero_point = zero_point
+        self.dtype = dtype
+
+    def forward(self, input_tensor):
+        quantized_tensor =  torch.quantize_per_tensor(input_tensor, self.scale, self.zero_point, self.dtype)
+        dequantized_tensor = torch.dequantize(quantized_tensor)
+        return dequantized_tensor
+
+class aten_quantize_per_channel_aten_dequantize(torch.nn.Module):
+    def __init__(self, scales, zero_points, dtype, axis) -> None:
+        torch.nn.Module.__init__(self)
+        self.scales = torch.Tensor(scales)
+        self.zero_points = torch.Tensor(zero_points)
+        self.dtype = dtype
+        self.axis = axis
+    def forward(self, input_tensor):
+        quantized_tensor =  torch.quantize_per_channel(input_tensor, self.scales, self.zero_points, self.axis, self.dtype)
+        dequantized_tensor = torch.dequantize(quantized_tensor)
+        return dequantized_tensor
+
+class TestQuantizePerTensorDequantize(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.array(5.00 * np.random.rand(100, 100) + 5.00, dtype=np.float32),)
+
+    @pytest.mark.parametrize("scale", [ 
+        1.0, 0.21, 0.62
+    ])
+    @pytest.mark.parametrize("zero_point", [
+        0, 4, -7
+    ])
+    @pytest.mark.parametrize("dtype", [
+        torch.quint8,
+        torch.qint8,
+        pytest.param(torch.qint32, marks=pytest.mark.skip(
+            reason="Not supported with FakeQuantize."))
+    ])
+    @pytest.mark.nightly
+    # @pytest.mark.precommit - sporadic issue
+    def test_quantize_per_tensor_dequantize(self, scale, zero_point, dtype, ie_device, precision, ir_version):
+        if dtype == torch.quint8: zero_point = abs(zero_point)
+        self._test(aten_quantize_per_tensor_aten_dequantize(scale, zero_point, dtype), None, ["aten::quantize_per_tensor", "aten::dequantize"], 
+                ie_device, precision, ir_version, )
+
+class TestQuantizePerChannelDequantize(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.array(5.00 * np.random.rand(5, 6, 7, 8) + 5.00, dtype=np.float32),)
+
+    @pytest.mark.parametrize("scales", [ 
+        np.array([1.0, 0.21, 0.62, 0.5], dtype=np.float32),
+        np.array([0.21, 0.62, 0.5, 1.0], dtype=np.float32),
+        np.array([0.62, 0.5, 1.0, 0.21], dtype=np.float32),
+        np.array([0.5, 1.0, 0.21, 0.62], dtype=np.float32),
+    ])
+    @pytest.mark.parametrize("zero_points", [
+        np.array([0, 4, 2, 1], dtype=np.int32),
+        np.array([0, 1, 2, 3], dtype=np.int32),
+        np.array([0, 0, 0, 0], dtype=np.int32),
+        np.array([-1, 0, -4, 5], dtype=np.int32),
+    ])
+    @pytest.mark.parametrize("dtype", [
+        torch.quint8,
+        torch.qint8,
+        pytest.param(torch.qint32, marks=pytest.mark.skip(
+            reason="Not supported with FakeQuantize."))
+    ])
+    @pytest.mark.parametrize("axis", [
+        0, 1, 2, 3
+    ])
+    @pytest.mark.nightly
+    # @pytest.mark.precommit - conversion issue
+    def test_quantize_per_channel_dequantize(self, scales, zero_points, dtype, axis, ie_device, precision, ir_version):
+        if dtype == torch.quint8: zero_points = abs(zero_points)
+        self._test(aten_quantize_per_channel_aten_dequantize(scales, zero_points, dtype, axis), None, ["aten::quantize_per_channel", "aten::dequantize"], 
+                ie_device, precision, ir_version, )