mirror of https://github.com/llvm/torch-mlir
45 lines
2.1 KiB
Python
45 lines
2.1 KiB
Python
|
# -*- Python -*-
|
||
|
|
# This file is licensed under a pytorch-style license
|
||
|
|
# See frontends/pytorch/LICENSE for license information.
|
||
|
|
|
||
|
|
import typing
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch_mlir
|
||
|
|
|
||
|
|
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
|
||
|
|
|
||
|
|
mb = torch_mlir.ModuleBuilder()
|
||
|
|
|
||
|
|
class TestModule(torch.nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
self.linear = torch.nn.quantized.Linear(5, 2, dtype=torch.qint8)
|
||
|
|
self.linear_no_bias = torch.nn.quantized.Linear(6,
|
||
|
|
2,
|
||
|
|
bias_=False,
|
||
|
|
dtype=torch.qint8)
|
||
|
|
# CHECK-DAG: %[[SCALE:.*]] = basicpy.numeric_constant {{.*}} : f64
|
||
|
|
# CHECK-DAG: %[[ZERO_POINT:.*]] = basicpy.numeric_constant 0 : i64
|
||
|
|
# CHECK-DAG: %[[INT_REPR:.*]] = constant dense<{{.*}}> : tensor<2x5xi8>
|
||
|
|
# CHECK-DAG: %[[WEIGHTS:.*]] = torch.per_tensor_affine.create %[[INT_REPR]], %[[SCALE]], %[[ZERO_POINT]] : tensor<2x5xi8>, f64, i64 -> tensor<2x5x!torch.qint8>
|
||
|
|
# CHECK-DAG: %[[WEIGHTS_ARRAY:.*]] = numpy.create_array_from_tensor %[[WEIGHTS]] : (tensor<2x5x!torch.qint8>) -> !numpy.ndarray<*:!numpy.any_dtype>
|
||
|
|
# CHECK-DAG: %[[BIAS:.*]] = constant dense<{{.*}}> : tensor<2xf32>
|
||
|
|
# CHECK-DAG: %[[BIAS_ARRAY:.*]] = numpy.create_array_from_tensor %[[BIAS]] : (tensor<2xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
|
||
|
|
# CHECK-DAG: %[[LINEAR_PARAMS:.*]] = torch.linear_params.create %[[WEIGHTS_ARRAY]], %[[BIAS_ARRAY]] : !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>
|
||
|
|
@torch.jit.export
|
||
|
|
def test_linear(self, t):
|
||
|
|
return self.linear(t)
|
||
|
|
|
||
|
|
# CHECK: %[[LINEAR_PARAMS_NO_BIAS:.*]] = torch.linear_params.create %{{.*}} : !numpy.ndarray<*:!numpy.any_dtype>{{$}}
|
||
|
|
@torch.jit.export
|
||
|
|
def test_linear_no_bias(self, t):
|
||
|
|
return self.linear_no_bias(t)
|
||
|
|
|
||
|
|
|
||
|
|
test_module = TestModule()
|
||
|
|
recursivescriptmodule = torch.jit.script(test_module)
|
||
|
|
# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
|
||
|
|
mb.import_module(recursivescriptmodule._c)
|
||
|
|
mb.module.operation.print()
|