mirror of
https://github.com/pytorch/pytorch.git
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[BE] Remove Model Dump utility (#141540)
So I found this utility by accident, trying to find how many html files we have in the repo so I could convert them to markdown Turns out we package some html and js files in pytorch to visualize torchscript models. This seems kinda strange, probably shouldn't be in core, I removed the tests I could find. Maybe some internal tests will break but considering torchscript is being superseded might make sense to do this Last time there was a meaningful update to the test for this file was about 2 years ago by @digantdesai since then it's a bunch of routine upgrades It seems like this package is unused https://github.com/search?type=code&auto_enroll=true&q=torch.utils.model_dump&p=1 I skimmed through 5 pages of these and the only time this shows up in code search is when someone is either cloning pytorch or checking in their venv into github Pull Request resolved: https://github.com/pytorch/pytorch/pull/141540 Approved by: https://github.com/malfet
This commit is contained in:
Mark Saroufim
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
533798ef46
commit
e24190709f
@ -156,7 +156,6 @@ coverage_ignore_functions = [
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"DistributedDataParallelCPU",
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# torch.utils
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"set_module",
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# torch.utils.model_dump
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"burn_in_info",
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"get_info_and_burn_skeleton",
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"get_inline_skeleton",
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@ -433,7 +433,6 @@ S390X_TESTLIST = [
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"test_mkldnn_verbose",
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"test_mkl_verbose",
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"test_mobile_optimizer",
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"test_model_dump",
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"test_model_exports_to_core_aten",
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"test_module_tracker",
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"test_monitor",
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@ -1,242 +0,0 @@
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#!/usr/bin/env python3
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# Owner(s): ["oncall: mobile"]
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import os
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import io
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import functools
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import tempfile
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import urllib
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import unittest
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import torch
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import torch.backends.xnnpack
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import torch.utils.model_dump
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import torch.utils.mobile_optimizer
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from torch.testing._internal.common_utils import TestCase, run_tests, IS_WINDOWS, skipIfNoXNNPACK
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from torch.testing._internal.common_quantized import supported_qengines
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class SimpleModel(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.layer1 = torch.nn.Linear(16, 64)
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self.relu1 = torch.nn.ReLU()
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self.layer2 = torch.nn.Linear(64, 8)
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self.relu2 = torch.nn.ReLU()
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def forward(self, features):
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act = features
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act = self.layer1(act)
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act = self.relu1(act)
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act = self.layer2(act)
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act = self.relu2(act)
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return act
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class QuantModel(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.quant = torch.ao.quantization.QuantStub()
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self.dequant = torch.ao.quantization.DeQuantStub()
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self.core = SimpleModel()
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def forward(self, x):
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x = self.quant(x)
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x = self.core(x)
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x = self.dequant(x)
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return x
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class ModelWithLists(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.rt = [torch.zeros(1)]
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self.ot = [torch.zeros(1), None]
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def forward(self, arg):
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arg = arg + self.rt[0]
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o = self.ot[0]
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if o is not None:
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arg = arg + o
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return arg
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def webdriver_test(testfunc):
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@functools.wraps(testfunc)
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def wrapper(self, *args, **kwds):
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self.needs_resources()
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if os.environ.get("RUN_WEBDRIVER") != "1":
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self.skipTest("Webdriver not requested")
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from selenium import webdriver
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for driver in [
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"Firefox",
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"Chrome",
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]:
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with self.subTest(driver=driver):
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wd = getattr(webdriver, driver)()
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testfunc(self, wd, *args, **kwds)
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wd.close()
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return wrapper
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class TestModelDump(TestCase):
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def needs_resources(self):
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pass
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def test_inline_skeleton(self):
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self.needs_resources()
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skel = torch.utils.model_dump.get_inline_skeleton()
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assert "unpkg.org" not in skel
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assert "src=" not in skel
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def do_dump_model(self, model, extra_files=None):
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# Just check that we're able to run successfully.
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buf = io.BytesIO()
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torch.jit.save(model, buf, _extra_files=extra_files)
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info = torch.utils.model_dump.get_model_info(buf)
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assert info is not None
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def open_html_model(self, wd, model, extra_files=None):
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buf = io.BytesIO()
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torch.jit.save(model, buf, _extra_files=extra_files)
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page = torch.utils.model_dump.get_info_and_burn_skeleton(buf)
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wd.get("data:text/html;charset=utf-8," + urllib.parse.quote(page))
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def open_section_and_get_body(self, wd, name):
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container = wd.find_element_by_xpath(f"//div[@data-hider-title='{name}']")
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caret = container.find_element_by_class_name("caret")
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if container.get_attribute("data-shown") != "true":
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caret.click()
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content = container.find_element_by_tag_name("div")
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return content
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def test_scripted_model(self):
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model = torch.jit.script(SimpleModel())
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self.do_dump_model(model)
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def test_traced_model(self):
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model = torch.jit.trace(SimpleModel(), torch.zeros(2, 16))
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self.do_dump_model(model)
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def test_main(self):
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self.needs_resources()
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if IS_WINDOWS:
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# I was getting tempfile errors in CI. Just skip it.
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self.skipTest("Disabled on Windows.")
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with tempfile.NamedTemporaryFile() as tf:
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torch.jit.save(torch.jit.script(SimpleModel()), tf)
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# Actually write contents to disk so we can read it below
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tf.flush()
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stdout = io.StringIO()
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torch.utils.model_dump.main(
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[
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None,
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"--style=json",
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tf.name,
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],
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stdout=stdout)
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self.assertRegex(stdout.getvalue(), r'\A{.*SimpleModel')
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stdout = io.StringIO()
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torch.utils.model_dump.main(
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[
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None,
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"--style=html",
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tf.name,
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],
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stdout=stdout)
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self.assertRegex(
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stdout.getvalue().replace("\n", " "),
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r'\A<!DOCTYPE.*SimpleModel.*componentDidMount')
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def get_quant_model(self):
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fmodel = QuantModel().eval()
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fmodel = torch.ao.quantization.fuse_modules(fmodel, [
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["core.layer1", "core.relu1"],
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["core.layer2", "core.relu2"],
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])
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fmodel.qconfig = torch.ao.quantization.get_default_qconfig("qnnpack")
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prepped = torch.ao.quantization.prepare(fmodel)
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prepped(torch.randn(2, 16))
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qmodel = torch.ao.quantization.convert(prepped)
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return qmodel
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@unittest.skipUnless("qnnpack" in supported_qengines, "QNNPACK not available")
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def test_quantized_model(self):
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qmodel = self.get_quant_model()
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self.do_dump_model(torch.jit.script(qmodel))
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@skipIfNoXNNPACK
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@unittest.skipUnless("qnnpack" in supported_qengines, "QNNPACK not available")
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def test_optimized_quantized_model(self):
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qmodel = self.get_quant_model()
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smodel = torch.jit.trace(qmodel, torch.zeros(2, 16))
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omodel = torch.utils.mobile_optimizer.optimize_for_mobile(smodel)
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self.do_dump_model(omodel)
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def test_model_with_lists(self):
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model = torch.jit.script(ModelWithLists())
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self.do_dump_model(model)
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def test_invalid_json(self):
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model = torch.jit.script(SimpleModel())
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self.do_dump_model(model, extra_files={"foo.json": "{"})
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@webdriver_test
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def test_memory_computation(self, wd):
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def check_memory(model, expected):
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self.open_html_model(wd, model)
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memory_table = self.open_section_and_get_body(wd, "Tensor Memory")
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device = memory_table.find_element_by_xpath("//table/tbody/tr[1]/td[1]").text
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self.assertEqual("cpu", device)
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memory_usage_str = memory_table.find_element_by_xpath("//table/tbody/tr[1]/td[2]").text
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self.assertEqual(expected, int(memory_usage_str))
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simple_model_memory = (
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# First layer, including bias.
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64 * (16 + 1) +
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# Second layer, including bias.
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8 * (64 + 1)
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# 32-bit float
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) * 4
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check_memory(torch.jit.script(SimpleModel()), simple_model_memory)
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# The same SimpleModel instance appears twice in this model.
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# The tensors will be shared, so ensure no double-counting.
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a_simple_model = SimpleModel()
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check_memory(
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torch.jit.script(
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torch.nn.Sequential(a_simple_model, a_simple_model)),
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simple_model_memory)
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# The freezing process will move the weight and bias
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# from data to constants. Ensure they are still counted.
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check_memory(
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torch.jit.freeze(torch.jit.script(SimpleModel()).eval()),
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simple_model_memory)
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# Make sure we can handle a model with both constants and data tensors.
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class ComposedModule(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.w1 = torch.zeros(1, 2)
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self.w2 = torch.ones(2, 2)
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def forward(self, arg):
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return arg * self.w2 + self.w1
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check_memory(
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torch.jit.freeze(
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torch.jit.script(ComposedModule()).eval(),
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preserved_attrs=["w1"]),
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4 * (2 + 4))
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if __name__ == '__main__':
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run_tests()
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@ -278,8 +278,6 @@ class TestPublicBindings(TestCase):
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for mod in pkgutil.walk_packages(torch.__path__, "torch.", onerror=onerror):
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modname = mod.name
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try:
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# TODO: fix "torch/utils/model_dump/__main__.py"
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# which calls sys.exit() when we try to import it
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if "__main__" in modname:
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continue
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importlib.import_module(modname)
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@ -76,6 +76,7 @@ import urllib.parse
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import zipfile
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from pathlib import Path
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from typing import Dict
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import warnings
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import torch.utils.show_pickle
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@ -389,6 +390,7 @@ def get_info_and_burn_skeleton(path_or_bytesio, **kwargs):
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def main(argv, *, stdout=None):
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warnings.warn("torch.utils.model_dump is deprecated and will be removed in a future PyTorch release.")
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parser = argparse.ArgumentParser()
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parser.add_argument("--style", choices=["json", "html"])
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parser.add_argument("--title")
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