mirror of https://github.com/llvm/torch-mlir
310 lines
10 KiB
Python
310 lines
10 KiB
Python
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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# Also available under a BSD-style license. See LICENSE.
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# RUN: %PYTHON %s | FileCheck %s
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import torch
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from framework import run_test
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from torch_mlir.eager_mode.torch_mlir_dispatch import build_script_function
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# CHECK: graph(%[[A1:.*]] : Tensor,
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# CHECK: %[[A2:.*]] : Tensor,
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# CHECK: %[[A3:.*]] : Tensor):
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# CHECK: %[[A4:.*]] : int = prim::Constant[value=1]()
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# CHECK: %[[A5:.*]] : int = prim::Constant[value=1]()
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# CHECK: %[[A0:.*]] : Tensor = aten::addmm(%[[A1]], %[[A2]], %[[A3]], %[[A4]], %[[A5]])
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# CHECK: return (%[[A0]])
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# -----
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# CHECK: PASS - simple
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@run_test
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def simple():
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target = torch.ops.aten.addmm.default
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A = torch.randn(1, 3, 32, 32)
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B = torch.randn(1, 3, 32, 32)
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C = torch.randn(1, 3, 32, 32)
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args = (A, B, C)
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kwargs = dict(beta=1, alpha=1)
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: graph(%[[B1:.*]] : Tensor,
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# CHECK: %[[B2:.*]] : Tensor,
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# CHECK: %[[B3:.*]] : Tensor):
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# CHECK: %[[B4:.*]] : int[] = prim::Constant[value=[1, 1]]()
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# CHECK: %[[B5:.*]] : int[] = prim::Constant[value=[0, 0]]()
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# CHECK: %[[B6:.*]] : int[] = prim::Constant[value=[1, 1]]()
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# CHECK: %[[B7:.*]] : bool = prim::Constant[value=0]()
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# CHECK: %[[B8:.*]] : int[] = prim::Constant[value=[0, 0]]()
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# CHECK: %[[B9:.*]] : int = prim::Constant[value=1]()
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# CHECK: %[[B0:.*]] : Tensor = aten::convolution(%[[B1]], %[[B2]], %[[B3]], %[[B4]], %[[B5]], %[[B6]], %[[B7]], %[[B8]], %[[B9]])
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# CHECK: return (%[[B0]])
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# -----
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# CHECK: PASS - handle_optional_tensor_input
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@run_test
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def handle_optional_tensor_input():
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target = torch.ops.aten.convolution.default
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input = torch.randn(1, 3, 32, 32)
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weight = torch.randn(3, 3, 3, 3)
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bias = torch.randn(3)
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args = (input, weight, bias)
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kwargs = dict(
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stride=[1, 1],
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padding=[0, 0],
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dilation=[1, 1],
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transposed=False,
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output_padding=[0, 0],
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groups=1,
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)
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: FAIL - fail_not_enough_args
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# CHECK: Errors: tuple index out of range
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@run_test
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def fail_not_enough_args():
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target = torch.ops.aten.convolution.default
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input = torch.randn(1, 3, 32, 32)
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weight = torch.randn(3, 3, 3, 3)
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bias = torch.randn(3)
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args = (input, weight, bias)
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kwargs = dict(
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stride=[1, 1],
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padding=[0, 0],
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dilation=[1, 1],
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transposed=False,
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output_padding=[0, 0],
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# Missing groups=1,
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)
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build_script_function(target._schema, args, kwargs)
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# CHECK: PASS - simple_args_or_kwargs
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@run_test
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def simple_args_or_kwargs():
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target = torch.ops.aten.convolution.default
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input = torch.randn(1, 3, 32, 32)
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weight = torch.randn(3, 3, 3, 3)
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bias = torch.randn(3)
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stride = [1, 1]
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padding = [0, 0]
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dilation = [1, 1]
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transposed = False
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output_padding = [0, 0]
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groups = 1
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script_fun1 = build_script_function(
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target._schema,
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(input, weight, bias),
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dict(
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stride=stride,
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padding=padding,
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dilation=dilation,
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transposed=transposed,
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output_padding=output_padding,
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groups=groups,
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),
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)
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script_fun2 = build_script_function(
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target._schema,
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(input, weight, bias, stride, padding, dilation),
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dict(transposed=transposed, output_padding=output_padding, groups=groups),
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)
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assert str(script_fun1.graph) == str(script_fun2.graph)
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# CHECK: graph(%[[C2:.*]] : Tensor):
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# CHECK: %[[C3:.*]] : int[] = prim::Constant[value=[3, 3]]()
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# CHECK: %[[C4:.*]] : NoneType = prim::Constant()
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# CHECK: %[[C5:.*]] : int[] = prim::Constant[value=[0, 0]]()
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# CHECK: %[[C6:.*]] : int[] = prim::Constant[value=[1, 1]]()
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# CHECK: %[[C7:.*]] : bool = prim::Constant[value=0]()
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# CHECK: %[[C0:.*]] : Tensor, %[[C1:.*]] : Tensor = aten::max_pool2d_with_indices(%[[C2]], %[[C3]], %[[C4]], %[[C5]], %[[C6]], %[[C7]])
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# CHECK: return (%[[C0]], %[[C1]])
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# -----
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# CHECK: PASS - handle_empty_lists
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@run_test
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def handle_empty_lists():
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target = torch.ops.aten.max_pool2d_with_indices.default
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# print(target._schema)
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args = (torch.randn((1, 3, 32, 32)),)
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kwargs = {
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"kernel_size": [3, 3],
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"stride": [],
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"padding": [0, 0],
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"dilation": [1, 1],
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"ceil_mode": False,
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}
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: graph(%[[D2:.*]] : Tensor):
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# CHECK: %[[D3:.*]] : int[] = prim::Constant[value=[3, 3]]()
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# CHECK: %[[D4:.*]] : NoneType = prim::Constant()
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# CHECK: %[[D5:.*]] : int[] = prim::Constant[value=[0, 0]]()
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# CHECK: %[[D6:.*]] : int[] = prim::Constant[value=[1, 1]]()
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# CHECK: %[[D7:.*]] : bool = prim::Constant[value=0]()
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# CHECK: %[[D0:.*]] : Tensor, %[[D1:.*]] : Tensor = aten::max_pool2d_with_indices(%[[D2]], %[[D3]], %[[D4]], %[[D5]], %[[D6]], %[[D7]])
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# CHECK: return (%[[D0]], %[[D1]])
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# -----
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# CHECK: PASS - handle_nones
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@run_test
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def handle_nones():
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target = torch.ops.aten.max_pool2d_with_indices.default
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# print(target._schema)
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args = (torch.randn((1, 3, 32, 32)),)
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kwargs = {
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"kernel_size": [3, 3],
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"stride": None,
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"padding": [0, 0],
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"dilation": [1, 1],
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"ceil_mode": False,
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}
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: graph(%[[E1:.*]] : Tensor,
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# CHECK: %[[E2:.*]] : Tensor,
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# CHECK: %[[E3:.*]] : Tensor):
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# CHECK: %[[E4:.*]] : int[] = prim::Constant[value=[1, 1]]()
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# CHECK: %[[E5:.*]] : int[] = prim::Constant[value=[0, 0]]()
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# CHECK: %[[E6:.*]] : int[] = prim::Constant[value=[1, 1]]()
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# CHECK: %[[E7:.*]] : bool = prim::Constant[value=0]()
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# CHECK: %[[E8:.*]] : int[] = prim::Constant[value=[0, 0]]()
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# CHECK: %[[E9:.*]] : int = prim::Constant[value=1]()
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# CHECK: %[[E0:.*]] : Tensor = aten::convolution(%[[E1]], %[[E2]], %[[E3]], %[[E4]], %[[E5]], %[[E6]], %[[E7]], %[[E8]], %[[E9]])
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# CHECK: return (%[[E0]])
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# -----
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# CHECK: PASS - handle_optional_tensors
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@run_test
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def handle_optional_tensors():
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target = torch.ops.aten.convolution.default
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input = torch.randn(1, 3, 32, 32)
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weight = torch.randn(3, 3, 3, 3)
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bias = torch.randn(3)
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args = (input, weight, bias)
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kwargs = dict(
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stride=[1, 1],
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padding=[0, 0],
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dilation=[1, 1],
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transposed=False,
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output_padding=[0, 0],
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groups=1,
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)
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: graph(%[[F1:.*]] : Tensor):
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# CHECK: %[[F2:.*]] : NoneType = prim::Constant()
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# CHECK: %[[F3:.*]] : NoneType = prim::Constant()
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# CHECK: %[[F4:.*]] : NoneType = prim::Constant()
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# CHECK: %[[F5:.*]] : NoneType = prim::Constant()
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# CHECK: %[[F6:.*]] : NoneType = prim::Constant()
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# CHECK: %[[F0:.*]] : Tensor = aten::ones_like(%[[F1]], %[[F2]], %[[F3]], %[[F4]], %[[F5]], %[[F6]])
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# CHECK: return (%[[F0]])
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# -----
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# CHECK: PASS - handle_ones_like
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@run_test
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def handle_ones_like():
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target = torch.ops.aten.ones_like.default
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input = torch.randn(1, 3, 32, 32)
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args = (input,)
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kwargs = dict(
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dtype=None, layout=None, device=None, pin_memory=None, memory_format=None
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)
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: graph(%[[G3:.*]] : Tensor,
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# CHECK: %[[G4:.*]] : Tensor,
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# CHECK: %[[G5:.*]] : Tensor):
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# CHECK: %[[G6:.*]] : NoneType = prim::Constant()
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# CHECK: %[[G7:.*]] : NoneType = prim::Constant()
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# CHECK: %[[G8:.*]] : bool = prim::Constant[value=0]()
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# CHECK: %[[G9:.*]] : float = prim::Constant[value=1.]()
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# CHECK: %[[G10:.*]] : float = prim::Constant[value=1.]()
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# CHECK: %[[G0:.*]] : Tensor, %[[G1:.*]] : Tensor, %[[G2:.*]] : Tensor = aten::native_batch_norm(%[[G3]], %[[G4]], %[[G5]], %[[G6]], %[[G7]], %[[G8]], %[[G9]], %[[G10]])
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# CHECK: return (%[[G0]], %[[G1]], %[[G2]])
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# -----
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# CHECK: PASS - handle_multiple_outputs
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@run_test
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def handle_multiple_outputs():
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target = torch.ops.aten.native_batch_norm.default
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A = torch.randn(1, 3, 32, 32)
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B = torch.randn(1, 3, 32, 32)
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C = torch.randn(1, 3, 32, 32)
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args = (A, B, C, None, None, False, 1.0, 1.0)
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kwargs = dict()
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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# CHECK: f
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# CHECK: PASS - check_legal_name
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@run_test
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def check_legal_name():
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target = torch.ops.aten.native_batch_norm.default
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A = torch.randn(1, 3, 32, 32)
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B = torch.randn(1, 3, 32, 32)
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C = torch.randn(1, 3, 32, 32)
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args = (A, B, C, None, None, False, 1.0, 1.0)
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kwargs = dict()
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.name)
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# CHECK: graph(%[[H3:.*]] : Tensor,
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# CHECK: %[[H4:.*]] : Tensor,
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# CHECK: %[[H5:.*]] : Tensor,
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# CHECK: %[[H6:.*]] : Tensor,
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# CHECK: %[[H7:.*]] : Tensor,
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# CHECK: %out : Tensor,
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# CHECK: %save_mean : Tensor,
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# CHECK: %save_invstd : Tensor):
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# CHECK: %[[H8:.*]] : bool = prim::Constant[value=0]()
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# CHECK: %[[H9:.*]] : float = prim::Constant[value=0.10000000000000001]()
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# CHECK: %[[H10:.*]] : float = prim::Constant[value=0.0001]()
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# CHECK: %[[H0:.*]] : Tensor, %[[H1:.*]] : Tensor, %[[H2:.*]] : Tensor = aten::native_batch_norm(%[[H3]], %[[H4]], %[[H5]], %[[H6]], %[[H7]], %[[H8]], %[[H9]], %[[H10]], %out, %save_mean, %save_invstd)
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# CHECK: return (%[[H0]], %[[H1]], %[[H2]])
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# -----
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# CHECK: PASS - correctly_order_kwargs
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@run_test
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def correctly_order_kwargs():
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target = torch.ops.aten.native_batch_norm.out
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input = torch.randn(2, 5, 2, 3)
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weight = torch.randn(5)
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bias = torch.randn(5)
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running_mean = torch.randn(5)
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running_var = torch.randn(5)
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args = (input, weight, bias, running_mean, running_var)
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out = torch.empty_like(input)
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save_mean = torch.empty_like(running_mean)
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save_invstd = torch.empty_like(running_var)
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kwargs = dict(
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training=False,
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momentum=0.1,
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eps=0.0001,
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out=out,
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save_mean=save_mean,
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save_invstd=save_invstd,
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)
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script_fun = build_script_function(target._schema, args, kwargs)
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print(script_fun.graph)
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