mirror of https://github.com/llvm/torch-mlir
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
# See https://llvm.org/LICENSE.txt for license information.
|
|
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
# Also available under a BSD-style license. See LICENSE.
|
|
|
|
# RUN: %PYTHON %s | FileCheck %s
|
|
|
|
|
|
import torch
|
|
|
|
from framework import run_test
|
|
from torch_mlir.eager_mode.torch_mlir_dispatch import (
|
|
annotate_args_kwargs,
|
|
normalize_args_kwargs,
|
|
build_script_function,
|
|
)
|
|
|
|
|
|
# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
|
|
# -----
|
|
# CHECK: PASS - simple
|
|
@run_test
|
|
def simple():
|
|
target = torch.ops.aten.addmm.default
|
|
A = torch.randn(1, 3, 32, 32)
|
|
B = torch.randn(1, 3, 32, 32)
|
|
C = torch.randn(1, 3, 32, 32)
|
|
args = (A, B, C)
|
|
kwargs = dict(beta=1, alpha=1)
|
|
|
|
new_args, new_kwargs = normalize_args_kwargs(target.overloadpacket, args, kwargs)
|
|
script_fun = build_script_function(target._schema, new_args, new_kwargs)
|
|
annotations, *_ = annotate_args_kwargs(script_fun, new_args, new_kwargs)
|
|
for annot in annotations:
|
|
print(annot)
|
|
|
|
|
|
# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
|
|
# -----
|
|
# CHECK: PASS - handle_zero_dim
|
|
@run_test
|
|
def handle_zero_dim():
|
|
target = torch.ops.aten.addmm.default
|
|
A = torch.randn(0, 3, 32, 32)
|
|
B = torch.randn(0, 3, 32, 32)
|
|
C = torch.randn(0, 3, 32, 32)
|
|
args = (A, B, C)
|
|
kwargs = dict(beta=1, alpha=1)
|
|
|
|
new_args, new_kwargs = normalize_args_kwargs(target.overloadpacket, args, kwargs)
|
|
script_fun = build_script_function(target._schema, new_args, new_kwargs)
|
|
annotations, *_ = annotate_args_kwargs(script_fun, new_args, new_kwargs)
|
|
for annot in annotations:
|
|
print(annot)
|
|
|
|
|
|
# CHECK: Torch Tensor (shape=(2, 5, 2, 3), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(2, 5, 2, 3), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
|
|
# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
|
|
# -----
|
|
# CHECK: PASS - correctly_order_kwargs
|
|
@run_test
|
|
def correctly_order_kwargs():
|
|
target = torch.ops.aten.native_batch_norm.out
|
|
|
|
input = torch.randn(2, 5, 2, 3)
|
|
weight = torch.randn(5)
|
|
bias = torch.randn(5)
|
|
running_mean = torch.randn(5)
|
|
running_var = torch.randn(5)
|
|
args = (input, weight, bias, running_mean, running_var)
|
|
|
|
out = torch.empty_like(input)
|
|
save_mean = torch.empty_like(running_mean)
|
|
save_invstd = torch.empty_like(running_var)
|
|
|
|
kwargs = dict(
|
|
training=False,
|
|
momentum=0.1,
|
|
eps=0.0001,
|
|
out=out,
|
|
save_mean=save_mean,
|
|
save_invstd=save_invstd,
|
|
)
|
|
|
|
new_args, new_kwargs = normalize_args_kwargs(target.overloadpacket, args, kwargs)
|
|
script_fun = build_script_function(target._schema, new_args, new_kwargs)
|
|
annotations, *_ = annotate_args_kwargs(script_fun, new_args, new_kwargs)
|
|
for annot in annotations:
|
|
print(annot)
|