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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
""" Test translate from relax. """
import pytest
import torch
from torch import fx
from torch.nn import Module
import numpy as np
import tvm.testing
from tvm.relax.frontend.torch import from_fx
from tvm.contrib.msc.core.frontend import translate
from tvm.contrib.msc.framework.tvm import codegen as tvm_codegen
def _verify_model(torch_model, input_info, opt_config=None):
graph_model = fx.symbolic_trace(torch_model)
with torch.no_grad():
orig_mod = from_fx(graph_model, input_info)
target = "llvm"
dev = tvm.cpu()
args = [tvm.nd.array(np.random.random(size=shape).astype(dtype)) for shape, dtype in input_info]
def _tvm_runtime_to_np(obj):
if isinstance(obj, tvm.runtime.NDArray):
return obj.numpy()
elif isinstance(obj, tvm.runtime.ShapeTuple):
return np.array(obj, dtype="int64")
elif isinstance(obj, (list, tvm.ir.container.Array)):
return [_tvm_runtime_to_np(item) for item in obj]
elif isinstance(obj, tuple):
return tuple(_tvm_runtime_to_np(item) for item in obj)
else:
return obj
def _run_relax(relax_mod):
relax_mod = tvm.relax.transform.LegalizeOps()(relax_mod)
relax_exec = tvm.relax.build(relax_mod, target)
vm_runner = tvm.relax.VirtualMachine(relax_exec, dev)
res = vm_runner["main"](*args)
return _tvm_runtime_to_np(res)
rt_mod = tvm_codegen.to_relax(
*translate.from_relax(orig_mod, opt_config=opt_config),
codegen_config={"explicit_name": False},
)
orig_output = _run_relax(orig_mod)
rt_output = _run_relax(rt_mod)
tvm.testing.assert_allclose(orig_output, rt_output)
def test_conv1d():
"""test relax translator for conv1d"""
class Conv1D1(Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv1d(3, 6, 7, bias=True)
def forward(self, data):
return self.conv(data)
class Conv1D2(Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv1d(3, 6, 7, bias=False)
def forward(self, data):
return self.conv(data)
input_info = [([1, 3, 10], "float32")]
_verify_model(Conv1D1(), input_info)
_verify_model(Conv1D2(), input_info)
def test_conv2d():
"""test relax translator for conv2d"""
class Conv2D1(Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 6, 7, bias=True)
def forward(self, data):
return self.conv(data)
class Conv2D2(Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 6, 7, bias=False)
def forward(self, data):
return self.conv(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Conv2D1(), input_info)
_verify_model(Conv2D2(), input_info)
def test_linear():
"""test relax translator for linear"""
class Dense1(Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 7, bias=True)
def forward(self, data):
return self.linear(data)
class Dense2(Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(10, 7, bias=False)
def forward(self, data):
return self.linear(data)
class MatMul1(Module):
def forward(self, x, y):
return torch.matmul(x, y)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Dense1(), input_info)
_verify_model(Dense2(), input_info)
_verify_model(MatMul1(), [([10, 10], "float32"), ([10, 10], "float32")])
def test_bmm():
"""test relax translator for bmm"""
class BMM(Module):
def forward(self, x, y):
return torch.bmm(x, y)
input_info = [((4, 128, 256), "float32"), ((4, 256, 512), "float32")]
_verify_model(BMM(), input_info)
def test_baddbmm():
"""test relax translator for baddbmm"""
class BAddBMM1(Module):
def forward(self, c, x, y):
return torch.baddbmm(c, x, y)
class BAddBMM2(Module):
def forward(self, c, x, y):
return torch.baddbmm(c, x, y, alpha=2, beta=0)
input_info = [
((4, 128, 512), "float32"),
((4, 128, 256), "float32"),
((4, 256, 512), "float32"),
]
_verify_model(BAddBMM1(), input_info)
_verify_model(BAddBMM2(), input_info)
def test_relu():
"""test relax translator for relu"""
class ReLU(Module):
def __init__(self):
super().__init__()
self.relu = torch.nn.ReLU()
def forward(self, data):
return self.relu(data)
class ReLU1(Module):
def forward(self, data):
return torch.nn.functional.relu(data)
input_info = [([10, 10], "float32")]
_verify_model(ReLU(), input_info)
_verify_model(ReLU1(), input_info)
def test_relu6():
"""test relax translator for relu6"""
class ReLU6(Module):
def __init__(self):
super().__init__()
self.relu6 = torch.nn.ReLU6()
def forward(self, data):
return self.relu6(data)
input_info = [([10, 10], "float32")]
_verify_model(ReLU6(), input_info)
def test_maxpool2d():
"""test relax translator for maxpool2d"""
class MaxPool2d(Module):
def __init__(self):
super().__init__()
self.pool = torch.nn.MaxPool2d(kernel_size=[1, 1])
def forward(self, data):
return self.pool(data)
class MaxPool2d2(Module):
def __init__(self):
super().__init__()
self.pool = torch.nn.MaxPool2d(kernel_size=[2, 2], dilation=[2, 3])
def forward(self, data):
return self.pool(data)
class MaxPool2d3(Module):
def __init__(self):
super().__init__()
self.pool = torch.nn.MaxPool2d(kernel_size=[4, 4], padding=2, stride=2)
def forward(self, data):
return self.pool(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(MaxPool2d(), input_info)
_verify_model(MaxPool2d2(), input_info)
_verify_model(MaxPool2d3(), input_info)
def test_avgpool2d():
"""test relax translator for avgpool2d"""
class AvgPool2d(Module):
def __init__(self):
super().__init__()
self.pool = torch.nn.AvgPool2d(kernel_size=[1, 1])
def forward(self, data):
return self.pool(data)
class AvgPool2d2(Module):
def __init__(self):
super().__init__()
self.pool = torch.nn.AvgPool2d(kernel_size=[4, 4], stride=2, padding=2, ceil_mode=True)
def forward(self, data):
return self.pool(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(AvgPool2d(), input_info)
_verify_model(AvgPool2d2(), input_info)
def test_adaptive_avgpool2d():
"""test relax translator for adaptive_avgpool2d"""
class AdaptiveAvgPool2d0(Module):
def __init__(self):
super().__init__()
self.pool = torch.nn.AdaptiveAvgPool2d([10, 10])
def forward(self, data):
return self.pool(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(AdaptiveAvgPool2d0(), input_info)
def test_flatten():
"""test relax translator for flatten"""
class Flatten(Module):
def __init__(self):
super().__init__()
self.f = torch.nn.Flatten(2, -1)
def forward(self, data):
return self.f(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Flatten(), input_info)
_verify_model(torch.nn.Flatten(2, -1), input_info)
def test_batchnorm2d():
"""test relax translator for batchnorm2d"""
class BatchNorm2d(Module):
def __init__(self):
super().__init__()
self.batchnorm = torch.nn.BatchNorm2d(3)
def forward(self, data):
return self.batchnorm(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(BatchNorm2d(), input_info)
def test_embedding():
"""test relax translator for embedding"""
class Embedding(Module):
def __init__(self):
super().__init__()
self.embedding = torch.nn.Embedding(10, 3)
def forward(self, data):
return self.embedding(data)
_verify_model(Embedding(), [([4], "int64")])
_verify_model(Embedding(), [([4, 5], "int64")])
def test_dropout():
"""test relax translator for dropout"""
class Dropout1(Module):
def __init__(self):
super().__init__()
self.dropout = torch.nn.Dropout(0.5)
def forward(self, data):
return self.dropout(data)
class Dropout2(Module):
def forward(self, data):
return torch.dropout(data, 0.5, train=True)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Dropout1(), input_info)
_verify_model(Dropout2(), input_info)
def test_layernorm():
"""test relax translator for layernorm"""
class LayerNorm(Module):
def __init__(self):
super().__init__()
self.layernorm = torch.nn.LayerNorm((10, 10))
def forward(self, data):
return self.layernorm(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(LayerNorm(), input_info)
def test_functional_layernorm():
"""test relax translator for functional_layernorm"""
class LayerNorm(Module):
def __init__(self, shape):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones(shape))
self.bias = torch.nn.Parameter(torch.zeros(shape))
def forward(self, data):
return torch.nn.functional.layer_norm(
data, self.weight.shape, self.weight, self.bias, 1e-5
)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(LayerNorm((10, 10)), input_info)
def test_cross_entropy():
"""test relax translator for cross_entropy"""
class CrossEntropy1(Module):
def __init__(self):
super().__init__()
self.loss = torch.nn.CrossEntropyLoss()
def forward(self, logits, targets):
return self.loss(logits, targets)
class CrossEntropy2(Module):
def __init__(self):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones((2,)))
self.loss = torch.nn.CrossEntropyLoss(weight=self.weight)
def forward(self, logits, targets):
return self.loss(logits, targets)
class CrossEntropy3(Module):
def __init__(self):
super().__init__()
self.loss = torch.nn.CrossEntropyLoss(ignore_index=1, reduction="sum")
def forward(self, logits, targets):
return self.loss(logits, targets)
input_info = [([3, 2], "float32"), ([3], "int32")]
_verify_model(CrossEntropy1(), input_info)
_verify_model(CrossEntropy2(), input_info)
_verify_model(CrossEntropy3(), input_info)
def test_functional_cross_entropy():
"""test relax translator for functional_cross_entropy"""
class CrossEntropy(Module):
def forward(self, logits, targets):
return torch.nn.functional.cross_entropy(logits, targets)
input_info = [([3, 10], "float32"), ([3], "int32")]
_verify_model(CrossEntropy(), input_info)
def test_silu():
"""test relax translator for silu"""
class SiLU(Module):
def __init__(self):
super().__init__()
self.silu = torch.nn.SiLU()
def forward(self, data):
return self.silu(data)
class SiLU2(Module):
def forward(self, data):
return torch.nn.functional.silu(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(SiLU(), input_info)
_verify_model(SiLU2(), input_info)
def test_groupnorm():
"""test relax translator for groupnorm"""
class GroupNorm(Module):
def __init__(self):
super().__init__()
self.groupnorm = torch.nn.GroupNorm(3, 3)
def forward(self, data):
return self.groupnorm(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(GroupNorm(), input_info)
def test_softmax():
"""test relax translator for softmax"""
class Softmax(Module):
def __init__(self):
super().__init__()
self.softmax = torch.nn.Softmax(dim=1)
def forward(self, data):
return self.softmax(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Softmax(), input_info)
def test_binary():
"""test relax translator for binary"""
input_info1 = [([1, 3, 10, 10], "float32"), ([1, 3, 10, 10], "float32")]
input_info2 = [([1, 3, 10, 10], "float32")]
# Add
class Add1(Module):
def forward(self, lhs, rhs):
return lhs + rhs
class Add2(Module):
def forward(self, lhs):
return lhs + 1.0
_verify_model(Add1(), input_info1)
_verify_model(Add2(), input_info2)
# Sub
class Sub1(Module):
def forward(self, lhs, rhs):
return lhs - rhs
class Sub2(Module):
def forward(self, lhs):
return lhs - 1.0
_verify_model(Sub1(), input_info1)
_verify_model(Sub2(), input_info2)
# Mul
class Mul1(Module):
def forward(self, lhs, rhs):
return lhs * rhs
class Mul2(Module):
def forward(self, lhs):
return lhs * 1.0
_verify_model(Mul1(), input_info1)
_verify_model(Mul2(), input_info2)
# True div
class TrueDiv1(Module):
def forward(self, lhs, rhs):
return lhs / rhs
class TrueDiv2(Module):
def forward(self, lhs):
return lhs / 1.0
_verify_model(TrueDiv1(), input_info1)
_verify_model(TrueDiv2(), input_info2)
# Floor div
class FloorDiv1(Module):
def forward(self, lhs, rhs):
return lhs // rhs
class FloorDiv2(Module):
def forward(self, lhs):
return lhs // 1.0
_verify_model(FloorDiv1(), input_info1)
_verify_model(FloorDiv2(), input_info2)
# Power
class Power1(Module):
def forward(self, lhs, rhs):
return lhs**rhs
class Power2(Module):
def forward(self, lhs):
return lhs**1.0
_verify_model(Power1(), input_info1)
_verify_model(Power2(), input_info2)
# LT
class LT1(Module):
def forward(self, lhs, rhs):
return lhs < rhs
class LT2(Module):
def forward(self, lhs):
return lhs < 1.0
_verify_model(LT1(), input_info1)
_verify_model(LT2(), input_info2)
def test_size():
"""test relax translator for size"""
class Size(Module):
def forward(self, data):
return data.size()
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Size(), input_info)
def test_squeeze():
"""test relax translator for squeeze"""
class Squeeze1(Module):
def forward(self, data):
return data.squeeze(1)
class Squeeze2(Module):
def forward(self, data):
return data.squeeze()
input_info = [([3, 1, 4, 1], "float32")]
_verify_model(Squeeze1(), input_info)
_verify_model(Squeeze2(), input_info)
def test_unsqueeze():
"""test relax translator for unsqueeze"""
class Unsqueeze1(Module):
def forward(self, data):
return data.unsqueeze(1)
class Unsqueeze2(Module):
def forward(self, data):
return data.unsqueeze(-1)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Unsqueeze1(), input_info)
_verify_model(Unsqueeze2(), input_info)
def test_getattr():
"""test relax translator for getattr"""
class GetAttr1(Module):
def forward(self, data):
return data.shape
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(GetAttr1(), input_info)
@pytest.mark.xfail(reason="MSC does not support Tuple of PrimValue")
def test_getitem():
"""test relax translator for getitem"""
class Slice1(Module):
def forward(self, x):
return x[0, 1::2, :, :3]
class Slice2(Module):
def forward(self, x):
return x[:, None, None, :, None]
_verify_model(Slice1(), [([1, 3, 10, 10], "float32")])
_verify_model(Slice2(), [([8, 16], "float32")])
def test_unary():
"""test relax translator for unary"""
input_info = [([1, 3, 10, 10], "float32")]
# sin
class Sin(Module):
def forward(self, data):
return torch.sin(data)
_verify_model(Sin(), input_info)
# cos
class Cos(Module):
def forward(self, data):
return torch.cos(data)
_verify_model(Cos(), input_info)
# exp
class Exp(Module):
def forward(self, data):
return torch.exp(data)
_verify_model(Exp(), input_info)
# sqrt
class Sqrt(Module):
def forward(self, data):
return torch.sqrt(data)
_verify_model(Sqrt(), input_info)
# sigmoid
class Sigmoid(Module):
def forward(self, data):
return torch.sigmoid(data)
_verify_model(Sigmoid(), input_info)
# round
class Round(Module):
def forward(self, data):
return torch.round(data)
_verify_model(Round(), input_info)
def test_gelu():
"""test relax translator for gelu"""
class Gelu(Module):
def forward(self, data):
return torch.nn.functional.gelu(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Gelu(), input_info)
def test_tanh():
"""test relax translator for tanh"""
class Tanh(Module):
def forward(self, data):
return torch.tanh(data)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Tanh(), input_info)
def test_clamp():
"""test relax translator for clamp"""
class Clamp(Module):
def forward(self, data):
return torch.clamp(data, min=0.1, max=0.5)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Clamp(), input_info)
def test_interpolate():
"""test relax translator for interpolate"""
class Interpolate(Module):
def forward(self, data):
return torch.nn.functional.interpolate(data, (5, 5))
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Interpolate(), input_info)
def test_addmm():
"""test relax translator for addmm"""
class Addmm(Module):
def forward(self, x_1, x_2, x_3):
return torch.addmm(x_1, x_2, x_3)
input_info = [
([10, 10], "float32"),
([10, 10], "float32"),
([10, 10], "float32"),
]
_verify_model(Addmm(), input_info)
def test_split():
"""test relax translator for split"""
class Split(Module):
def forward(self, data):
return torch.split(data, 1, dim=1)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Split(), input_info)
def test_cumsum():
"""test relax translator for cumsum"""
class Cumsum(Module):
def forward(self, data):
return torch.cumsum(data, dim=1, dtype=torch.int32)
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(Cumsum(), input_info)
def test_chunk():
"""test relax translator for chunk"""
class Chunk(Module):
def forward(self, data):
return torch.chunk(data, 3, dim=1)
input_info = [([1, 3, 10, 10], "float32")]
_verify_model(Chunk(), input_info)
def test_inplace_fill():
"""test relax translator for inplace_fill"""
class InplaceFill(Module):
def forward(self, data):
data.fill_(1.5)
return data
_verify_model(InplaceFill(), [([10, 10], "float32")], opt_config={"opt_level": 0})
def test_arange():
"""test relax translator for arange"""
class Arange(Module):
def forward(self):
return torch.arange(0, 20, dtype=torch.int32)
_verify_model(Arange(), [([10, 10], "float32")])
def test_empty():
"""test relax translator for empty"""
class Empty(Module):
def forward(self):
return torch.empty((10, 10), dtype=torch.float32)
_verify_model(Empty(), [([10, 10], "float32")])
def test_tensor():
"""test relax translator for tensor"""
class Empty1(Module):
def forward(self):
return torch.tensor(3, dtype=torch.float32)
class Empty2(Module):
def forward(self):
return torch.tensor(3)
_verify_model(Empty1(), [([10, 10], "float32")])
_verify_model(Empty2(), [([10, 10], "float32")])
def test_tril():
"""test relax translator for tril"""
class Tril(Module):
def forward(self, data):
return torch.tril(data, 1)
class InplaceTril(Module):
def forward(self, data):
data.tril_(1)
return data
input_info = [([10, 10], "float32")]
_verify_model(Tril(), input_info)
_verify_model(InplaceTril(), input_info)
def test_triu():
"""test relax translator for triu"""
class Triu(Module):
def forward(self, data):
return torch.triu(data, 1)
class InplaceTriu(Module):
def forward(self, data):
data.triu_(1)
return data
input_info = [([10, 10], "float32")]
_verify_model(Triu(), input_info)
_verify_model(InplaceTriu(), input_info)
def test_new_ones():
"""test relax translator for new_ones"""
class NewOnes(Module):
def forward(self, x):
return x.new_ones(1, 2, 3)
input_info = [([1, 2, 3], "float32")]
_verify_model(NewOnes(), input_info, opt_config={"opt_level": 0})
def test_expand():
"""test relax translator for expand"""
class Expand(Module):
def forward(self, x):
return x.expand(4, 2, 3, 4)
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(Expand(), input_info)
def test_reduce():
"""test relax translator for reduce"""
# sum
class Sum(Module):
def forward(self, x):
return torch.sum(x, (2, 1))
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(Sum(), input_info)
def test_datatype():
"""test relax translator for datatype"""
input_info = [([1, 2, 3, 4], "float32")]
# float
class ToFloat(Module):
def forward(self, x):
return x.float()
_verify_model(ToFloat(), input_info)
# half
class ToHalf(Module):
def forward(self, x):
return x.half()
_verify_model(ToHalf(), input_info)
# type
class Type(Module):
def forward(self, x):
return x.type(torch.float32)
# type
class TypeFromAttr(Module):
def forward(self, x):
return x.type(x.getattr("dtype"))
# astype
class AsType(Module):
def forward(self, x):
return x.astype(torch.float32)
_verify_model(Type(), input_info)
_verify_model(TypeFromAttr(), input_info)
_verify_model(AsType(), input_info)
def test_permute():
"""test relax translator for permute"""
class Permute(Module):
def forward(self, x):
return x.permute(0, 3, 2, 1)
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(Permute(), input_info)
def test_reshape():
"""test relax translator for reshape"""
class Reshape(Module):
def forward(self, x):
return x.reshape(2, 12)
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(Reshape(), input_info)
def test_transpose():
"""test relax translator for transpose"""
class Transpose(Module):
def forward(self, x):
return x.transpose(1, 3)
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(Transpose(), input_info)
def test_view():
"""test relax translator for view"""
class View(Module):
def forward(self, x):
return x.view(2, 12)
input_info = [([1, 2, 3, 4], "float32")]
_verify_model(View(), input_info)
def test_keep_params():
"""test relax translator for keep_params"""
class Conv2D1(Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(3, 6, 7, bias=True)
def forward(self, data):
return self.conv(data)
_verify_model(Conv2D1(), [([1, 3, 10, 10], "float32")])
def test_unwrap_unit_return_tuple():
"""test relax translator for unwrap_unit_return_tuple"""
class Identity(Module):
def forward(self, x):
return (x,)
_verify_model(Identity(), [([256, 256], "float32")])
def test_no_bind_return_tuple():
"""test relax translator for no_bind_return_tuple"""
class Identity(Module):
def forward(self, x, y):
return (x, y)
input_info = [([256, 256], "float32"), ([256, 256], "float32")]
_verify_model(Identity(), input_info)
def test_argmax():
"""test relax translator for argmax"""
class Argmax1(Module):
def forward(self, data):
return torch.argmax(data, dim=-1)
class Argmax2(Module):
def forward(self, data):
return torch.argmax(data, dim=-1, keepdim=True)
_verify_model(Argmax1(), [([256, 256], "float32")])
_verify_model(Argmax2(), [([256, 256], "float32")])
def test_argmin():
"""test relax translator for argmin"""
class Argmin1(Module):
def forward(self, data):
return torch.argmin(data)
class Argmin2(Module):
def forward(self, data):
return torch.argmin(data, keepdim=True)
_verify_model(Argmin1(), [([256, 256], "float32")])
_verify_model(Argmin2(), [([256, 256], "float32")])
def test_to():
"""test relax translator for to"""
class To1(Module):
def forward(self, data):
return data.to(torch.float16)
class To2(Module):
def forward(self, data):
return data.to("cpu")
_verify_model(To1(), [([256, 256], "float32")])
_verify_model(To2(), [([256, 256], "float32")])
def test_mean():
"""test relax translator for mean"""
class Mean(Module):
def forward(self, data):
return data.mean(-1)
class MeanKeepDim(Module):
def forward(self, data):
return data.mean(-1, keepdim=True)
_verify_model(Mean(), [([256, 256], "float32")])
_verify_model(MeanKeepDim(), [([256, 256], "float32")])
def test_rsqrt():
"""test relax translator for rsqrt"""
class Rsqrt(Module):
def forward(self, data):
return torch.rsqrt(data)
_verify_model(Rsqrt(), [([256, 256], "float32")])
def test_neg():
"""test relax translator for neg"""
class Neg(Module):
def forward(self, data):
return -data
_verify_model(Neg(), [([256, 256], "float32")])
def test_max():
"""test relax translator for max"""
class Max(Module):
def forward(self, x, y):
return torch.max(x, y)
_verify_model(Max(), [([256, 256], "float32"), ([256, 256], "float32")])
def test_attention():
"""test relax translator for attention"""
# pylint: disable=import-outside-toplevel
import torch.nn.functional as F
class Attention1(Module):
def forward(self, q_data, k_data, v_data):
return F.scaled_dot_product_attention(q_data, k_data, v_data)
class Attention2(Module):
def forward(self, q_data, k_data, v_data):
return F.scaled_dot_product_attention(q_data, k_data, v_data, is_causal=True)
input_info = [
([32, 8, 128, 64], "float32"),
([32, 8, 128, 64], "float32"),
([32, 8, 128, 64], "float32"),
]
_verify_model(Attention1(), input_info)
_verify_model(Attention2(), input_info)
class Attention3(Module):
def forward(self, q_data, k_data, v_data, mask):
return F.scaled_dot_product_attention(q_data, k_data, v_data, mask)
_verify_model(
Attention3(),
[
([32, 8, 128, 64], "float32"),
([32, 8, 128, 64], "float32"),
([32, 8, 128, 64], "float32"),
([32, 8, 128, 128], "float32"),
],
)
if __name__ == "__main__":
tvm.testing.main()
