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Removing grid lowering #2686

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Apr 17, 2024
Merged

Removing grid lowering #2686

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2 changes: 2 additions & 0 deletions py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -332,6 +332,8 @@ def aten_ops_fmod(

@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler)
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler_2d)
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler.default)
@dynamo_tensorrt_converter(torch.ops.aten.grid_sampler_2d.default)
@enforce_tensor_types(
{
0: (TRTTensor,),
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1 change: 0 additions & 1 deletion py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py
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Expand Up @@ -46,7 +46,6 @@
aten.gelu,
aten.gelu_backward,
aten.glu_backward,
aten.grid_sampler_2d,
aten.hardshrink,
aten.hardshrink_backward,
aten.hardsigmoid,
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171 changes: 106 additions & 65 deletions tests/py/dynamo/conversion/test_grid_aten.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,112 +6,74 @@
from torch.testing._internal.common_utils import run_tests
from torch_tensorrt import Input

grid_sampler_aten_ops = {
"torch.ops.aten.grid_sampler": torch.ops.aten.grid_sampler,
"torch.ops.aten.grid_sampler_2d": torch.ops.aten.grid_sampler_2d,
"torch.ops.aten.grid_sampler.default": torch.ops.aten.grid_sampler.default,
"torch.ops.aten.grid_sampler_2d.default": torch.ops.aten.grid_sampler_2d.default,
}

grid_sampler_ops = [
(
"input_grid_interpolation_nearest_sample_fill",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 0, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 0, 0, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_nearest_sample_clamp",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 1, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 0, 1, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_nearest_sample_reflect",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 2, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 0, 2, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_linear_sample_fill",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 0, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 1, 0, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_linear_sample_clamp",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 1, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 1, 1, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_linear_sample_reflect",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 2, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 1, 2, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_cubic_sample_fill",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 0, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 2, 0, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_cubic_sample_clamp",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 1, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 2, 1, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_cubic_sample_reflect",
(lambda x, grid: torch.ops.aten.grid_sampler(x, grid, 0, 2, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_nearest_sample_fill_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 0, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_nearest_sample_clamp_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 1, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_nearest_sample_reflect_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 2, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_linear_sample_fill_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 0, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_linear_sample_clamp_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 1, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_linear_sample_reflect_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 2, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_cubic_sample_fill_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 0, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_cubic_sample_clamp_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 1, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
(
"input_grid_interpolation_cubic_sample_reflect_2d",
(lambda x, grid: torch.ops.aten.grid_sampler_2d(x, grid, 0, 2, True)),
"torch.ops.aten.grid_sampler",
(lambda x, grid, op: op(x, grid, 2, 2, True)),
[1, 1, 5, 5],
[1, 5, 2, 2],
),
Expand All @@ -126,19 +88,98 @@ class TestGridConverter(DispatchTestCase):
grid_sampler_op[1],
grid_sampler_op[2],
grid_sampler_op[3],
grid_sampler_op[4],
)
for grid_sampler_op in grid_sampler_ops
]
)
def test_grid(self, _, op_name, op, input_shape, dim_shape):
class TestModule(nn.Module):
def __init__(self, grid_sampler_op):
super().__init__()
self.grid_sampler_op = grid_sampler_op

def forward(self, x):
grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
return self.grid_sampler_op(x, grid, grid_sampler_aten_ops[op_name])
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Would this need to be grid_sampler_aten_ops[op_name](x, grid)?

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@gs-olive yes it could be but then in that case it would be something like-

   def test_grid(self, _, op_name, input_shape, dim_shape, padding_mode, interpolation_mode, align_corners):
        class TestModule(nn.Module):
            def __init__(self):
                super().__init__()

            def forward(self, x):
                grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
                return grid_sampler_aten_ops[op_name](x, grid, padding_mode, interpolation_mode, align_corners)

        inputs = [torch.randn(input_shape, dtype=torch.float32)]
        grid_model = TestModule()
        self.run_test(grid_model, inputs)

It would be code design choice to either encapsulate it in the lambda function in the parameters, or declare it in the above way. In my opinion both should be good.

You could let me know if you think otherwise.

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I see - this makes sense, thanks. In that case, it seems good to go - should just need a rebase to enable testing.


inputs = [torch.randn(input_shape, dtype=torch.float32)]
grid_model = TestModule(op)
self.run_test(grid_model, inputs)

@parameterized.expand(
[
(
grid_sampler_op[0],
grid_sampler_op[1] + "_2d",
grid_sampler_op[2],
grid_sampler_op[3],
grid_sampler_op[4],
)
for grid_sampler_op in grid_sampler_ops
]
)
def test_grid_2d(self, _, op_name, op, input_shape, dim_shape):
class TestModule(nn.Module):
def __init__(self, grid_sampler_op):
super().__init__()
self.grid_sampler_op = grid_sampler_op

def forward(self, x):
grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
return self.grid_sampler_op(x, grid, grid_sampler_aten_ops[op_name])

inputs = [torch.randn(input_shape, dtype=torch.float32)]
grid_model = TestModule(op)
self.run_test(grid_model, inputs)

@parameterized.expand(
[
(
grid_sampler_op[0],
grid_sampler_op[1] + ".default",
grid_sampler_op[2],
grid_sampler_op[3],
grid_sampler_op[4],
)
for grid_sampler_op in grid_sampler_ops
]
)
def test_grid_default(self, _, op_name, op, input_shape, dim_shape):
class TestModule(nn.Module):
def __init__(self, grid_sampler_op):
super().__init__()
self.grid_sampler_op = grid_sampler_op

def forward(self, x):
grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
return self.grid_sampler_op(x, grid, grid_sampler_aten_ops[op_name])

inputs = [torch.randn(input_shape, dtype=torch.float32)]
grid_model = TestModule(op)
self.run_test(grid_model, inputs)

@parameterized.expand(
[
(
grid_sampler_op[0],
grid_sampler_op[1] + "_2d.default",
grid_sampler_op[2],
grid_sampler_op[3],
grid_sampler_op[4],
)
for grid_sampler_op in grid_sampler_ops
]
)
def test_grid(self, _, op, input_shape, dim_shape):
def test_grid_2d_default(self, _, op_name, op, input_shape, dim_shape):
class TestModule(nn.Module):
def __init__(self, grid_sampler_op):
super().__init__()
self.grid_sampler_op = grid_sampler_op

def forward(self, x):
grid = torch.randint(-1, 1, dim_shape, dtype=torch.float32)
return self.grid_sampler_op(x, grid)
return self.grid_sampler_op(x, grid, grid_sampler_aten_ops[op_name])

inputs = [torch.randn(input_shape, dtype=torch.float32)]
grid_model = TestModule(op)
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