Piotr Krzemiński
e77070890a
* [PT FE] Fix sporadics with round & 0 zero_pt, 1.0 scale * [PT FE] Change scale and round input in quantized cat tests * [PT FE] Add rounding to conv & linear tests * Update test_quantized_cat.py * Update test_quantized_cat.py * [PT FE] Replace randn with rand for consistency in convnd
90 lines
4.3 KiB
Python
90 lines
4.3 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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import torch
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import numpy as np
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from pytorch_layer_test_class import PytorchLayerTest
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class TestQuantizedLinear(PytorchLayerTest):
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def _prepare_input(self, input_shape=(2, 2)):
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return (np.round(np.random.rand(*input_shape).astype(np.float32), 4),)
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def create_model(self, weight_shape, is_bias, scale, zero_point):
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class aten_quantized_linear(torch.nn.Module):
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def __init__(self, weight_shape, is_bias, scale, zero_point):
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super(aten_quantized_linear, self).__init__()
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if is_bias:
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self.linear = torch.ao.nn.quantized.Linear(
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weight_shape[-1], weight_shape[0], True)
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torch.nn.init.normal_(self.linear.bias())
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else:
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self.linear = torch.ao.nn.quantized.Linear(
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weight_shape[-1], weight_shape[0], False)
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self.linear.scale = float(scale)
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self.linear.zero_point = int(zero_point)
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def forward(self, inp):
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inp_q = torch.quantize_per_tensor(inp, 1.0, 0, torch.quint8)
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return torch.dequantize(self.linear(inp_q))
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ref_net = None
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return aten_quantized_linear(weight_shape, is_bias, scale, zero_point), ref_net, "quantized::linear"
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def create_hardtanh_model(self, weight_shape, is_bias, scale, zero_point, inplace):
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class aten_quantized_linear(torch.nn.Module):
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def __init__(self, weight_shape, is_bias, scale, zero_point, inplace):
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super(aten_quantized_linear, self).__init__()
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self.hardtanh = torch.nn.Hardtanh(inplace=inplace)
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if is_bias:
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self.linear = torch.ao.nn.quantized.Linear(
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weight_shape[-1], weight_shape[0], True)
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torch.nn.init.normal_(self.linear.bias())
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else:
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self.linear = torch.ao.nn.quantized.Linear(
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weight_shape[-1], weight_shape[0], False)
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self.linear.scale = float(scale)
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self.linear.zero_point = int(zero_point)
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def forward(self, inp):
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inp_q = torch.quantize_per_tensor(inp, 1., 0, torch.quint8)
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inp_q = self.hardtanh(inp_q)
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return torch.dequantize(self.linear(inp_q))
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ref_net = None
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return aten_quantized_linear(weight_shape, is_bias, scale, zero_point, inplace), ref_net, ["quantized::linear", "aten::hardtanh_" if inplace else "aten::hardtanh"]
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@pytest.mark.parametrize("params", [
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{'input_shape': [3, 9], 'weight_shape': [10, 9]},
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{'input_shape': [3, 9], 'weight_shape': [9]},
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{'input_shape': [2, 3, 9], 'weight_shape': [10, 9]},
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{'input_shape': [2, 3, 9], 'weight_shape': [9]},
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{'input_shape': [3, 9], 'weight_shape': [9], "bias": True},
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{'input_shape': [3, 9], 'weight_shape': [10, 9], "bias": True},
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{'input_shape': [2, 3, 9], 'weight_shape': [10, 9], "bias": True},
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])
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@pytest.mark.parametrize("scale", [1., 0.3, 1.3])
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@pytest.mark.parametrize("zero_point", [0, 1])
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@pytest.mark.parametrize("trace", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_quantized_linear(self, params, scale, zero_point, trace, ie_device, precision, ir_version):
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input_shape = params.get("input_shape")
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weight_shape = params.get("weight_shape")
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bias = params.get("bias", False)
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self._test(*self.create_model(weight_shape, bias, scale, zero_point), ie_device, precision, ir_version,
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kwargs_to_prepare_input={"input_shape": input_shape}, trace_model=trace, freeze_model=False, quantized_ops=True, quant_size=scale)
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@pytest.mark.parametrize("trace", [True, False])
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@pytest.mark.parametrize("inplace", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_quantized_hardtanh_linear(self, trace, inplace, ie_device, precision, ir_version):
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self._test(*self.create_hardtanh_model([10, 9], True, 1, 0.3, inplace), ie_device, precision, ir_version,
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kwargs_to_prepare_input={"input_shape": [2, 3, 9]}, trace_model=trace, freeze_model=False, quantized_ops=True, quant_size=0.3)
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