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Map ops from FFE emit of TTIR to MLIR if it is supported #549

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Oct 28, 2024
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Map ops from FFE emit of TTIR to MLIR if it is supported #549

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6 changes: 6 additions & 0 deletions forge/csrc/passes/lower_to_mlir.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -567,6 +567,12 @@ class MLIRGenerator
lowering_handler_map["concatenate"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::ConcatOp>;
lowering_handler_map["sigmoid"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::SigmoidOp>;
lowering_handler_map["max_pool2d"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::MaxPool2dOp>;
lowering_handler_map["abs"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::AbsOp>;
lowering_handler_map["exp"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::ExpOp>;
lowering_handler_map["maximum"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::MaximumOp>;
lowering_handler_map["less"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::LessEqualOp>;
lowering_handler_map["greater"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::GreaterThanOp>;
lowering_handler_map["not_equal"] = &MLIRGenerator::emit_mlir_ttforge_op<mlir::tt::ttir::NotEqualOp>;
}
};
} // namespace
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199 changes: 199 additions & 0 deletions forge/test/mlir/test_ops.py
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Expand Up @@ -13,6 +13,205 @@
from forge.op.eval.common import compare_with_golden_pcc, compare_with_golden
from forge.tensor import to_forge_tensors, to_pt_tensors

shapes = [(1, 1, 256, 256), (1, 1, 1, 128), (1, 1, 1, 384), (1, 1, 32, 32), (1, 1, 6, 6), (1, 1, 29, 29)]


@pytest.mark.parametrize("shape", shapes)
def test_abs(shape):
class abs(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return torch.abs(x)

inputs = [torch.rand(shape)]

framework_model = abs()
fw_out = framework_model(*inputs)

compiled_model = forge.compile(framework_model, sample_inputs=inputs)
co_out = compiled_model(*inputs)

co_out = [co.to("cpu") for co in co_out]
fw_out = [fw_out] if isinstance(fw_out, torch.Tensor) else fw_out
assert all([compare_with_golden_pcc(golden=fo, calculated=co, pcc=0.99) for fo, co in zip(fw_out, co_out)])


shapes = [
(1, 128, 28, 28),
(1, 64, 28, 28),
(1, 256, 28, 28),
(1, 128, 14, 14),
(1, 128, 56, 56),
(1, 32, 64, 64),
(1, 512, 7, 7),
]


@pytest.mark.parametrize("shape", shapes)
def test_exp(shape):
class exp(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return torch.exp(x)

inputs = [torch.rand(shape)]

framework_model = exp()
fw_out = framework_model(*inputs)

compiled_model = forge.compile(framework_model, sample_inputs=inputs)
co_out = compiled_model(*inputs)

co_out = [co.to("cpu") for co in co_out]
fw_out = [fw_out] if isinstance(fw_out, torch.Tensor) else fw_out
assert all([compare_with_golden_pcc(golden=fo, calculated=co, pcc=0.99) for fo, co in zip(fw_out, co_out)])


shapes = [
((1, 12, 256, 256), (1,)),
((1, 16, 256, 256), (1,)),
((1, 32, 256, 256), (1,)),
((1, 12, 32, 32), (1,)),
((1, 16, 32, 32), (1,)),
((1, 32, 32, 32), (1,)),
]


@pytest.mark.parametrize("shape_x, shape_y", shapes)
def test_maximum(shape_x, shape_y):
class maximum(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, y):
return torch.maximum(x, y)

x = torch.rand(shape_x)
y = torch.rand(shape_y)

framework_model = maximum()
inputs = [x, y]

fw_out = framework_model(*inputs)
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
co_out = compiled_model(*inputs)

co_out = [co.to("cpu") for co in co_out]
fw_out = [fw_out] if isinstance(fw_out, torch.Tensor) else fw_out
assert all([compare_with_golden_pcc(golden=fo, calculated=co, pcc=0.99) for fo, co in zip(fw_out, co_out)])


shapes = [
((1, 128, 28, 28), (1, 128, 28, 28)),
((1, 64, 28, 28), (1, 64, 28, 28)),
((1, 256, 28, 28), (1, 256, 28, 28)),
((1, 128, 14, 14), (1, 128, 14, 14)),
((1, 128, 56, 56), (1, 128, 56, 56)),
((1, 32, 64, 64), (1, 32, 64, 64)),
((1, 512, 7, 7), (1, 512, 7, 7)),
((1, 32, 32, 32), (1, 32, 32, 32)),
]


@pytest.mark.parametrize("shape_x, shape_y", shapes)
def test_less(shape_x, shape_y):
class less(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, y):
return torch.less(x, y)

x = torch.rand(shape_x)
y = torch.rand(shape_y)

framework_model = less()
inputs = [x, y]

fw_out = framework_model(*inputs)
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
co_out = compiled_model(*inputs)

co_out = [co.to("cpu") for co in co_out]
fw_out = [fw_out] if isinstance(fw_out, torch.Tensor) else fw_out
assert all([compare_with_golden_pcc(golden=fo, calculated=co, pcc=0.99) for fo, co in zip(fw_out, co_out)])


shapes = [
((1, 128, 28, 28), (1, 128, 28, 28)),
((1, 64, 28, 28), (1, 64, 28, 28)),
((1, 256, 28, 28), (1, 256, 28, 28)),
((1, 128, 14, 14), (1, 128, 14, 14)),
((1, 128, 56, 56), (1, 128, 56, 56)),
((1, 32, 64, 64), (1, 32, 64, 64)),
((1, 512, 7, 7), (1, 512, 7, 7)),
((1, 32, 32, 32), (1, 32, 32, 32)),
]


@pytest.mark.parametrize("shape_x, shape_y", shapes)
def test_greater_op(shape_x, shape_y):
class greater(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, y):
return torch.greater(x, y)

x = torch.rand(shape_x)
y = torch.rand(shape_y)

framework_model = greater()
inputs = [x, y]

fw_out = framework_model(*inputs)
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
co_out = compiled_model(*inputs)

co_out = [co.to("cpu") for co in co_out]
fw_out = [fw_out] if isinstance(fw_out, torch.Tensor) else fw_out
assert all([compare_with_golden_pcc(golden=fo, calculated=co, pcc=0.99) for fo, co in zip(fw_out, co_out)])


shapes = [
((1, 128, 28, 28), (1, 128, 28, 28)),
((1, 64, 28, 28), (1, 64, 28, 28)),
((1, 256, 28, 28), (1, 256, 28, 28)),
((1, 128, 14, 14), (1, 128, 14, 14)),
((1, 128, 56, 56), (1, 128, 56, 56)),
((1, 32, 64, 64), (1, 32, 64, 64)),
((1, 512, 7, 7), (1, 512, 7, 7)),
((1, 32, 32, 32), (1, 32, 32, 32)),
]


@pytest.mark.parametrize("shape_x, shape_y", shapes)
def test_not_equal(shape_x, shape_y):
class not_equal(nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, y):
return torch.ne(x, y)

x = torch.rand(shape_x)
y = torch.rand(shape_y)

framework_model = not_equal()
inputs = [x, y]

fw_out = framework_model(*inputs)
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
co_out = compiled_model(*inputs)

co_out = [co.to("cpu") for co in co_out]
fw_out = [fw_out] if isinstance(fw_out, torch.Tensor) else fw_out
assert all([compare_with_golden_pcc(golden=fo, calculated=co, pcc=0.99) for fo, co in zip(fw_out, co_out)])


@pytest.mark.parametrize(
"batch_size, num_channels, height, width",
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