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[reland][quant][fix] Compare resnet with quantizer api with the prepare_fx and decomposed convert flow (#99355)
Summary: Using a decomposed convert to make sure we get exact match, this means the nodes in resnet are annotated correctly, reland for https://github.com/pytorch/pytorch/pull/98905 Test Plan: python test/test_quantization.py TestQuantizePT2EModels.test_resnet18_with_quantizer_api Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D45071168](https://our.internmc.facebook.com/intern/diff/D45071168) Pull Request resolved: https://github.com/pytorch/pytorch/pull/99355 Approved by: https://github.com/kimishpatel
This commit is contained in:
Jerry Zhang
PyTorch MergeBot
parent
391a3add54
commit
b0df0cd7cc
3 changed files with 69 additions and 69 deletions
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@ -48,7 +48,6 @@ from torch.ao.quantization import (
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default_reuse_input_qconfig,
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default_symmetric_qnnpack_qconfig,
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default_symmetric_qnnpack_qat_qconfig,
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default_per_channel_symmetric_qnnpack_qconfig,
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per_channel_dynamic_qconfig,
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float16_dynamic_qconfig,
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float16_static_qconfig,
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@ -192,7 +191,6 @@ from torch.testing._internal.common_quantized import (
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from torch.testing._internal.common_utils import (
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TemporaryFileName,
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IS_ARM64,
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IS_WINDOWS,
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)
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from torch.testing._internal.common_quantization import NodeSpec as ns
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@ -6163,57 +6161,6 @@ class TestQuantizeFx(QuantizationTestCase):
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res = m(*example_inputs)
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self.assertEqual(res, res_ref)
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@unittest.skipIf(IS_WINDOWS, "torch.compile is not supported on Windows")
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def test__convert_to_reference_decomposed_fx_per_channel_quant_module(self):
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""" Test the result for per channel weight quant for reference modules
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"""
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = torch.nn.Conv2d(3, 3, 3)
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def forward(self, x):
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return self.conv(x)
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m = M().eval()
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qconfig_mapping = QConfigMapping().set_global(default_per_channel_symmetric_qnnpack_qconfig)
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example_inputs = (torch.randn(1, 3, 10, 10),)
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m = prepare_fx(m, qconfig_mapping, example_inputs, backend_config=get_qnnpack_backend_config())
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m(*example_inputs)
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m_ref = copy.deepcopy(m)
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m_ref = convert_to_reference_fx(m_ref, backend_config=get_qnnpack_backend_config())
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m = _convert_to_reference_decomposed_fx(m, backend_config=get_qnnpack_backend_config())
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expected_occurrence = {
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# for input and output activations
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ns.call_function(torch.ops.quantized_decomposed.quantize_per_tensor.default): 2,
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ns.call_function(torch.ops.quantized_decomposed.dequantize_per_tensor.default): 2,
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# weight is per channel quantized
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ns.call_function(torch.ops.quantized_decomposed.quantize_per_channel.default): 1,
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ns.call_function(torch.ops.quantized_decomposed.dequantize_per_channel.default): 1,
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}
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import torch._dynamo as torchdynamo
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m, guards = torchdynamo.export(
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m,
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*copy.deepcopy(example_inputs),
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aten_graph=True,
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tracing_mode="real",
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)
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self.checkGraphModuleNodes(
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m,
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expected_node_occurrence=expected_occurrence)
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# make sure it runs
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res_ref = m_ref(*example_inputs)
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res = m(*example_inputs)
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self.assertEqual(res, res_ref)
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# check the qmin/qmax for per channel quant
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for n in m.graph.nodes:
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if n.op == "call_function" and \
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n.target == torch.ops.quantized_decomposed.quantize_per_channel.default:
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_QUANT_MIN_INDEX = 4
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_QUANT_MAX_INDEX = 5
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self.assertEqual(n.args[_QUANT_MIN_INDEX], -127)
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self.assertEqual(n.args[_QUANT_MAX_INDEX], 127)
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def test_change_backend_config_for_fixed_qparam_ops(self):
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""" Making sure we can skip validation of qconfigs for fixedqparam ops based
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on BackendConfig
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@ -265,11 +265,5 @@ class TestQuantizePT2EModels(QuantizationTestCase):
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compute_sqnr(after_prepare_result, after_prepare_result_fx),
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torch.tensor(float("inf")),
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)
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# there are slight differences after convert due to different implementations
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# of quant/dequant
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self.assertTrue(
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torch.max(after_quant_result - after_quant_result_fx) < 1e-1
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)
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self.assertTrue(
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compute_sqnr(after_quant_result, after_quant_result_fx) > 35
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)
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self.assertEqual(after_quant_result, after_quant_result_fx)
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self.assertTrue(compute_sqnr(after_quant_result, after_quant_result_fx) == torch.tensor(float("inf")))
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@ -7,7 +7,12 @@ import torch._dynamo as torchdynamo
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import torch.nn as nn
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from torch._inductor.compile_fx import compile_fx
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from torch.ao.ns.fx.utils import compute_sqnr
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from torch.ao.quantization import get_default_qconfig, observer, QConfigMapping
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from torch.ao.quantization import (
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get_default_qconfig,
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observer,
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QConfigMapping,
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default_per_channel_symmetric_qnnpack_qconfig,
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)
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from torch.ao.quantization._quantize_pt2e import (
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convert_pt2e,
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prepare_pt2e,
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@ -23,8 +28,13 @@ from torch.ao.quantization.backend_config.x86 import get_x86_backend_config
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from torch.ao.quantization.quantize_fx import (
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convert_fx,
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convert_to_reference_fx,
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_convert_to_reference_decomposed_fx,
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prepare_fx,
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)
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from torch.testing._internal.common_utils import (
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IS_WINDOWS,
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)
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from torch.testing._internal.common_quantization import (
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NodeSpec as ns,
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QuantizationTestCase,
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@ -33,8 +43,10 @@ from torch.testing._internal.common_quantization import (
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skipIfNoX86,
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)
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from torch.testing._internal.common_quantized import override_quantized_engine
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import unittest
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# TODO: remove after quantizer API is more mature
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@skipIfNoQNNPACK
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class TestQuantizePT2EFX(QuantizationTestCase):
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def test_qconfig_none(self):
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@ -255,6 +267,57 @@ class TestQuantizePT2EFX(QuantizationTestCase):
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}
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self.checkGraphModuleNodes(m, expected_node_occurrence=node_occurrence)
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# TODO(jerryzh168): move all _convert_to_reference_decomposed_fx tests here
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@unittest.skipIf(IS_WINDOWS, "torch.compile is not supported on Windows")
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def test__convert_to_reference_decomposed_fx_per_channel_quant_module(self):
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""" Test the result for per channel weight quant for reference modules
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"""
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = torch.nn.Conv2d(3, 3, 3)
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def forward(self, x):
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return self.conv(x)
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m = M().eval()
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qconfig_mapping = QConfigMapping().set_global(default_per_channel_symmetric_qnnpack_qconfig)
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example_inputs = (torch.randn(1, 3, 10, 10),)
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m = prepare_fx(m, qconfig_mapping, example_inputs, backend_config=get_qnnpack_backend_config())
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m(*example_inputs)
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m_ref = copy.deepcopy(m)
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m_ref = convert_to_reference_fx(m_ref, backend_config=get_qnnpack_backend_config())
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m = _convert_to_reference_decomposed_fx(m, backend_config=get_qnnpack_backend_config())
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expected_occurrence = {
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# for input and output activations
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ns.call_function(torch.ops.quantized_decomposed.quantize_per_tensor.default): 2,
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ns.call_function(torch.ops.quantized_decomposed.dequantize_per_tensor.default): 2,
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# weight is per channel quantized
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ns.call_function(torch.ops.quantized_decomposed.quantize_per_channel.default): 1,
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ns.call_function(torch.ops.quantized_decomposed.dequantize_per_channel.default): 1,
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}
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import torch._dynamo as torchdynamo
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m, guards = torchdynamo.export(
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m,
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*copy.deepcopy(example_inputs),
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aten_graph=True,
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tracing_mode="real",
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)
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self.checkGraphModuleNodes(
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m,
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expected_node_occurrence=expected_occurrence)
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# make sure it runs
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res_ref = m_ref(*example_inputs)
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res = m(*example_inputs)
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self.assertEqual(res, res_ref)
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# check the qmin/qmax for per channel quant
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for n in m.graph.nodes:
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if n.op == "call_function" and \
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n.target == torch.ops.quantized_decomposed.quantize_per_channel.default:
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_QUANT_MIN_INDEX = 4
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_QUANT_MAX_INDEX = 5
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self.assertEqual(n.args[_QUANT_MIN_INDEX], -127)
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self.assertEqual(n.args[_QUANT_MAX_INDEX], 127)
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@skipIfNoQNNPACK
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class TestQuantizePT2EFXX86Inductor(QuantizationTestCase):
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@ -425,7 +488,7 @@ class TestQuantizePT2EFXModels(QuantizationTestCase):
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m_copy, qconfig_mapping, example_inputs, backend_config=backend_config
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)
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after_prepare_result_fx = m_fx(*example_inputs)
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m_fx = convert_to_reference_fx(m_fx, backend_config=backend_config)
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m_fx = _convert_to_reference_decomposed_fx(m_fx, backend_config=backend_config)
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after_quant_result_fx = m_fx(*example_inputs)
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@ -437,9 +500,5 @@ class TestQuantizePT2EFXModels(QuantizationTestCase):
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)
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# there are slight differences after convert due to different implementations
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# of quant/dequant
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self.assertTrue(
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torch.max(after_quant_result - after_quant_result_fx) < 1e-1
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)
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self.assertTrue(
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compute_sqnr(after_quant_result, after_quant_result_fx) > 35
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)
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self.assertTrue(torch.max(after_quant_result - after_quant_result_fx) < 1e-1)
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self.assertTrue(compute_sqnr(after_quant_result, after_quant_result_fx) > 35)
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