Clean up stale examples.

They were confusing users, and most didn't even work anymore.

frontends/pytorch/examples/cos_e2e.py

@@ -1,32 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-torch.manual_seed(0)
-input = torch.rand(2, 3)
-
-mb = torch_mlir.ModuleBuilder()
-with mb.capture_function("cos", [input]) as f:
-  result = torch.cos(input)
-  f.returns([result])
-
-backend = iree.IreeNpcompBackend()
-jit_module = backend.load(backend.compile(frontend_lowering.lower_module(mb.module)))
-
-logging.debug(f"Executing jit_module.cos")
-test_utils.compare_outputs(torch.cos, jit_module.cos, input)
-
-# This fails because ModuleBuilder represents torch.cos with a constant:
-#   https://github.com/llvm/mlir-npcomp/issues/135
-test_utils.compare_outputs(torch.cos, jit_module.cos, input + 1)

frontends/pytorch/examples/div_inplace_e2e.py

@@ -1,32 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-torch.manual_seed(0)
-
-arg0 = torch.ones(2, 2)
-arg1 = torch.ones(2, 2)
-
-def fun(a, b):
-  return a.div_(b)
-
-mb = torch_mlir.ModuleBuilder()
-with mb.capture_function("test", [arg0, arg1]) as f:
-  f.returns([fun(arg0, arg1)])
-
-backend = iree.IreeNpcompBackend()
-jit_module = backend.load(backend.compile(frontend_lowering.lower_module(mb.module)))
-
-test_utils.compare_outputs(torch.mm, jit_module.test, arg0, arg1)
-test_utils.compare_outputs(torch.mm, jit_module.test, arg0 + 1, arg1 + 1)

frontends/pytorch/examples/mm_e2e.py

@@ -1,29 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-torch.manual_seed(0)
-lhs = torch.rand(2, 3)
-rhs = torch.rand(3, 4)
-
-mb = torch_mlir.ModuleBuilder()
-with mb.capture_function("mm", [lhs, rhs]) as f:
-  result = torch.mm(lhs, rhs)
-  f.returns([result])
-
-backend = iree.IreeNpcompBackend()
-jit_module = backend.load(backend.compile(frontend_lowering.lower_module(mb.module)))
-
-test_utils.compare_outputs(torch.mm, jit_module.mm, lhs, rhs)
-test_utils.compare_outputs(torch.mm, jit_module.mm, lhs + 1, rhs - 1)

frontends/pytorch/examples/mul_maximum_e2e.py

@@ -1,37 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-lhs = torch.ones((4, 6, 1))
-rhs = torch.ones((1, 1, 3)) * 0.6
-bias = torch.ones((1, 1, 3)) * 0.2
-threshold = torch.tensor((0.75, 0.25, 0.10))
-
-
-def mul_maximum(lhs, rhs, threshold, bias):
-  return torch.maximum(lhs * rhs, threshold) + bias
-
-
-mb = torch_mlir.ModuleBuilder()
-with mb.capture_function("mul_maximum", [lhs, rhs, threshold, bias]) as f:
-  result = mul_maximum(lhs, rhs, threshold, bias)
-  f.returns([result])
-
-backend = iree.IreeNpcompBackend()
-jit_module = backend.load(backend.compile(frontend_lowering.lower_module(mb.module)))
-
-test_utils.compare_outputs(mul_maximum, jit_module.mul_maximum, lhs, rhs,
-                           threshold, bias)
-test_utils.compare_outputs(mul_maximum, jit_module.mul_maximum, lhs + 1,
-                           rhs + 2, threshold, bias)

frontends/pytorch/examples/resnet_inference.ipynb

@@ -94,9 +94,7 @@
     "from mlir.passmanager import PassManager\n",
     "\n",
     "from torch_mlir_torchscript.annotations import annotate_args, export\n",
-    "from torch_mlir.torchscript_annotations import extract_annotations\n",
+    "from torch_mlir.torchscript_annotations import extract_annotations"
-    "\n",
-    "from npcomp.compiler.pytorch.backend.iree import IreeNpcompBackend"
    ]
   },
   {

frontends/pytorch/examples/tanh_out_e2e.py

@@ -1,33 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-torch.manual_seed(0)
-
-arg0 = torch.ones(2, 2)
-
-def fun(a):
-  z = torch.zeros(2, 2)
-  torch.tanh(a, out=z)
-  return z
-
-mb = torch_mlir.ModuleBuilder()
-with mb.capture_function("test", [arg0]) as f:
-  f.returns([fun(arg0)])
-
-backend = iree.IreeNpcompBackend()
-jit_module = backend.load(backend.compile(frontend_lowering.lower_module(mb.module)))
-
-test_utils.compare_outputs(torch.mm, jit_module.test, arg0, arg1)
-test_utils.compare_outputs(torch.mm, jit_module.test, arg0 + 1, arg1 + 1)

frontends/pytorch/examples/test_utils.py

@@ -1,30 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import sys
-import textwrap
-
-import numpy as np
-
-INDENT = "  "
-
-
-def _indent(value):
-  return textwrap.indent(str(value), INDENT)
-
-
-def compare_outputs(torch_func, jit_func, *args):
-  print('-' * 80)
-
-  print(f"Input args:\n{_indent(args)}", file=sys.stderr)
-  result = torch_func(*args)
-  jit_result = jit_func(*args)
-
-  np.testing.assert_allclose(result.numpy(), jit_result, rtol=1e-05, atol=1e-08)
-
-  # Only print these if the test passes, as np.testing will print them if it
-  # fails.
-  print("SUCCESS", file=sys.stderr)
-  print(f"PyTorch Result:\n{_indent(result.numpy())}", file=sys.stderr)
-  print(f"JIT Result:\n{_indent(jit_result)}", file=sys.stderr)

frontends/pytorch/examples/torchscript_mm_e2e.py

@@ -1,58 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import typing
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
-
-mb = torch_mlir.ModuleBuilder()
-
-class Submodule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-    def forward(self, lhs, rhs):
-        return torch.mm(lhs, rhs)
-
-class TestModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.s = Submodule()
-    def forward(self, lhs, rhs):
-        return self.s.forward(lhs, rhs)
-
-test_module = TestModule()
-class_annotator = torch_mlir.ClassAnnotator()
-recursivescriptmodule = torch.jit.script(test_module)
-torch.jit.save(recursivescriptmodule, '/tmp/foo.pt')
-
-class_annotator.exportNone(recursivescriptmodule._c._type())
-class_annotator.exportPath(recursivescriptmodule._c._type(), ['forward'])
-class_annotator.annotateArgs(recursivescriptmodule._c._type(), ['forward'], [
-    None,
-    ([-1, -1], torch.float32),
-    ([-1, -1], torch.float32),
-])
-# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
-mb.import_module(recursivescriptmodule._c, class_annotator)
-#mb.module.operation.print()
-
-backend = iree.IreeNpcompBackend()
-compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
-jit_module = backend.load(compiled)
-
-torch.manual_seed(0)
-lhs = torch.rand(2, 3)
-rhs = torch.rand(3, 4)
-test_utils.compare_outputs(test_module.forward, jit_module.forward, lhs, rhs)

frontends/pytorch/examples/torchscript_tanh_e2e.py

@@ -1,49 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import typing
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import refjit, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-#logging.enable()
-
-# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
-
-mb = torch_mlir.ModuleBuilder()
-
-class TestModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-    def forward(self, x):
-        return torch.tanh(x)
-
-test_module = TestModule()
-class_annotator = torch_mlir.ClassAnnotator()
-recursivescriptmodule = torch.jit.script(test_module)
-torch.jit.save(recursivescriptmodule, '/tmp/foo.pt')
-
-class_annotator.exportNone(recursivescriptmodule._c._type())
-class_annotator.exportPath(recursivescriptmodule._c._type(), ['forward'])
-class_annotator.annotateArgs(recursivescriptmodule._c._type(), ['forward'], [
-    None,
-    ([2, 3, -1], torch.float32)
-])
-# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
-mb.import_module(recursivescriptmodule._c, class_annotator)
-#mb.module.operation.print()
-
-backend = iree.IreeNpcompBackend()
-compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
-jit_module = backend.load(compiled)
-
-torch.manual_seed(0)
-input = torch.rand(2, 3, 1)
-test_utils.compare_outputs(test_module.forward, jit_module.forward, input)

frontends/pytorch/examples/torchscript_tanh_e2e_iree.py

@@ -1,49 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-import typing
-
-import torch
-import torch_mlir
-
-import npcomp
-from npcomp.compiler.pytorch.backend import iree, frontend_lowering
-from npcomp.compiler.utils import logging
-
-import test_utils
-
-logging.enable()
-
-# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
-
-mb = torch_mlir.ModuleBuilder()
-
-class TestModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-    def forward(self, x):
-        return torch.tanh(x)
-
-test_module = TestModule()
-class_annotator = torch_mlir.ClassAnnotator()
-recursivescriptmodule = torch.jit.script(test_module)
-torch.jit.save(recursivescriptmodule, '/tmp/foo.pt')
-
-class_annotator.exportNone(recursivescriptmodule._c._type())
-class_annotator.exportPath(recursivescriptmodule._c._type(), ['forward'])
-class_annotator.annotateArgs(recursivescriptmodule._c._type(), ['forward'], [
-    None,
-    ([2, 3, -1], torch.float32, True)
-])
-# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
-mb.import_module(recursivescriptmodule._c, class_annotator)
-#mb.module.operation.print()
-
-backend = iree.IreeNpcompBackend()
-compiled = backend.compile(frontend_lowering.lower_object_graph(mb.module))
-jit_module = backend.load(compiled)
-
-torch.manual_seed(0)
-input = torch.rand(2, 3, 1)
-test_utils.compare_outputs(test_module.forward, jit_module.forward, input)