Add test for deepseek_math (#1148) #463
GitHub Actions / TT-Forge-FE Tests
failed
Feb 5, 2025 in 0s
568 tests run, 24 passed, 79 skipped, 465 failed.
Annotations
Check failure on line 77 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_less[shape_x0-shape_y0]
RuntimeError: Fatal error
Raw output
shape_x = (1, 128, 28, 28), shape_y = (1, 128, 28, 28)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
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)
inputs = [x, y]
framework_model = Less()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:77:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c90430>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0...e-01, 6.38527e-01],
[4.80435e-01, 5.63519e-01, 1.80081e-01, ..., 6.29593e-01, 1.39392e-01, 3.02127e-01]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0...e-01, 6.38527e-01],
[4.80435e-01, 5.63519e-01, 1.80081e-01, ..., 6.29593e-01, 1.39392e-01, 3.02127e-01]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 77 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_less[shape_x2-shape_y2]
RuntimeError: Fatal error
Raw output
shape_x = (1, 256, 28, 28), shape_y = (1, 256, 28, 28)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
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)
inputs = [x, y]
framework_model = Less()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:77:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c5bf10>
inputs = (tensor([[[[4.96257e-01, 7.68222e-01, 8.84774e-02, ..., 5.52907e-01, 9.52738e-01, 3.61648e-02],
[1.85231e-0... 0.67269, ..., 0.52853, 0.02791, 0.72723],
[0.50627, 0.20671, 0.06682, ..., 0.64299, 0.17580, 0.76558]]]]))
inputs_and_parameters = [tensor([[[[4.96257e-01, 7.68222e-01, 8.84774e-02, ..., 5.52907e-01, 9.52738e-01, 3.61648e-02],
[1.85231e-0... 0.67269, ..., 0.52853, 0.02791, 0.72723],
[0.50627, 0.20671, 0.06682, ..., 0.64299, 0.17580, 0.76558]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 77 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_less[shape_x4-shape_y4]
RuntimeError: Fatal error
Raw output
shape_x = (1, 128, 56, 56), shape_y = (1, 128, 56, 56)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
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)
inputs = [x, y]
framework_model = Less()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:77:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2dac730>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0... 0.24537, ..., 0.20951, 0.17554, 0.22976],
[0.29514, 0.97587, 0.57722, ..., 0.83887, 0.36270, 0.54569]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0... 0.24537, ..., 0.20951, 0.17554, 0.22976],
[0.29514, 0.97587, 0.57722, ..., 0.83887, 0.36270, 0.54569]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 77 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_less[shape_x6-shape_y6]
RuntimeError: Fatal error
Raw output
shape_x = (1, 512, 7, 7), shape_y = (1, 512, 7, 7)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
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)
inputs = [x, y]
framework_model = Less()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:77:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2cea470>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0... 0.53460, ..., 0.32252, 0.38427, 0.26611],
[0.86218, 0.91274, 0.66447, ..., 0.31686, 0.59966, 0.20105]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0... 0.53460, ..., 0.32252, 0.38427, 0.26611],
[0.86218, 0.91274, 0.66447, ..., 0.31686, 0.59966, 0.20105]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 110 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_greater[shape_x1-shape_y1]
RuntimeError: Fatal error
Raw output
shape_x = (1, 64, 28, 28), shape_y = (1, 64, 28, 28)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_greater(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)
inputs = [x, y]
framework_model = Greater()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:110:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c90340>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0... 0.33696, ..., 0.07847, 0.66520, 0.74933],
[0.38415, 0.30600, 0.06413, ..., 0.89975, 0.98978, 0.35272]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0... 0.33696, ..., 0.07847, 0.66520, 0.74933],
[0.38415, 0.30600, 0.06413, ..., 0.89975, 0.98978, 0.35272]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 110 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_greater[shape_x3-shape_y3]
RuntimeError: Fatal error
Raw output
shape_x = (1, 128, 14, 14), shape_y = (1, 128, 14, 14)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_greater(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)
inputs = [x, y]
framework_model = Greater()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:110:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c96e00>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.40172, 0.02233, 0.16886],
[0.29389, 0.51852, 0.69767, ..., 0... 0.86184, ..., 0.05997, 0.37089, 0.73024],
[0.12554, 0.51805, 0.53460, ..., 0.31686, 0.59966, 0.20105]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.40172, 0.02233, 0.16886],
[0.29389, 0.51852, 0.69767, ..., 0... 0.86184, ..., 0.05997, 0.37089, 0.73024],
[0.12554, 0.51805, 0.53460, ..., 0.31686, 0.59966, 0.20105]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 110 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_greater[shape_x5-shape_y5]
ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[[False, False, False, ..., True, False, False],
[ True, False, True, ..., True, False, False],
[False, False, True, ..., False, True, True],
...,
[ True, False, True, ..., True, True, False],
[ True, True, False, ..., False, False, False],
[ True, False, True, ..., True, True, False]],
[[ True, False, False, ..., True, True, False],
[False, True, False, ..., True, False, False],
[ True, True, True, ..., False, False, False],
...,
[ True, False, True, ..., False, False, False],
[False, False, True, ..., True, False, True],
[ True, False, False, ..., True, True, False]],
[[False, True, True, ..., False, False, False],
[False, True, True, ..., True, True, False],
[ True, True, True, ..., True, False, False],
...,
[False, False, True, ..., False, True, False],
[False, False, True, ..., True, False, False],
[ True, True, False, ..., False, True, False]],
...,
[[ True, True, True, ..., True, False, True],
[False, True, False, ..., False, True, False],
[ True, True, True, ..., True, False, True],
...,
[ True, False, False, ..., False, False, True],
[ True, False, False, ..., True, True, False],
[False, False, True, ..., True, False, True]],
[[ True, False, True, ..., True, True, True],
[ True, False, True, ..., False, True, True],
[ True, False, True, ..., False, False, True],
...,
[False, True, True, ..., True, False, False],
[ True, True, False, ..., True, False, False],
[ True, True, False, ..., True, True, True]],
[[False, True, True, ..., True, True, True],
[ True, False, True, ..., True, True, True],
[False, True, True, ..., True, False, True],
...,
[False, True, False, ..., True, False, True],
[ True, True, False, ..., False, True, True],
[False, True, False, ..., False, False, True]]]]), compiled_model=tensor([[[[0., 0., 0., ..., 1., 0., 1.],
[0., 1., 0., ..., 0., 1., 0.],
[0., 0., 1., ..., 0., 1., 1.],
...,
[0., 1., 0., ..., 0., 0., 0.],
[1., 0., 1., ..., 0., 0., 0.],
[1., 1., 0., ..., 1., 1., 0.]],
[[1., 0., 0., ..., 0., 0., 1.],
[0., 1., 1., ..., 0., 1., 1.],
[1., 1., 0., ..., 0., 1., 1.],
...,
[0., 0., 0., ..., 0., 1., 0.],
[0., 1., 1., ..., 0., 1., 1.],
[0., 1., 0., ..., 1., 1., 0.]],
[[0., 1., 1., ..., 1., 0., 0.],
[0., 1., 0., ..., 1., 0., 1.],
[1., 0., 1., ..., 0., 0., 1.],
...,
[1., 0., 1., ..., 1., 0., 1.],
[0., 1., 1., ..., 0., 1., 1.],
[0., 0., 1., ..., 0., 1., 0.]],
...,
[[1., 1., 1., ..., 0., 1., 0.],
[1., 0., 1., ..., 1., 0., 0.],
[1., 0., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 0., 0.],
[1., 1., 1., ..., 1., 0., 1.],
[1., 1., 1., ..., 1., 0., 1.]],
[[1., 0., 1., ..., 0., 0., 1.],
[0., 0., 0., ..., 0., 1., 1.],
[0., 1., 1., ..., 1., 1., 0.],
...,
[0., 1., 0., ..., 0., 0., 1.],
[1., 0., 1., ..., 0., 0., 0.],
[1., 0., 0., ..., 1., 1., 1.]],
[[0., 1., 1., ..., 0., 1., 1.],
[1., 1., 0., ..., 1., 1., 1.],
[1., 0., 0., ..., 0., 1., 0.],
...,
[1., 0., 1., ..., 0., 1., 0.],
[0., 1., 0., ..., 0., 0., 0.],
[1., 1., 0., ..., 0., 0., 1.]]]])Raw output
shape_x = (1, 32, 64, 64), shape_y = (1, 32, 64, 64)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_greater(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)
inputs = [x, y]
framework_model = Greater()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:110:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:333: in verify
verify_cfg.value_checker.check(fw, co)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.verify.value_checkers.AutomaticValueChecker object at 0x7fb619fc4580>
fw_out = tensor([[[[False, False, False, ..., True, False, False],
[ True, False, True, ..., True, False, False]... [ True, True, False, ..., False, True, True],
[False, True, False, ..., False, False, True]]]])
co_out = tensor([[[[0., 0., 0., ..., 1., 0., 1.],
[0., 1., 0., ..., 0., 1., 0.],
[0., 0., 1., ..., 0., 1... [1., 0., 1., ..., 0., 1., 0.],
[0., 1., 0., ..., 0., 0., 0.],
[1., 1., 0., ..., 0., 0., 1.]]]])
def check(self, fw_out, co_out):
if not compare_with_golden(fw_out, co_out, self.pcc, self.rtol, self.atol, self.dissimilarity_threshold):
> raise ValueError(
f"Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model={fw_out}, compiled_model={co_out}"
)
E ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[[False, False, False, ..., True, False, False],
E [ True, False, True, ..., True, False, False],
E [False, False, True, ..., False, True, True],
E ...,
E [ True, False, True, ..., True, True, False],
E [ True, True, False, ..., False, False, False],
E [ True, False, True, ..., True, True, False]],
E
E [[ True, False, False, ..., True, True, False],
E [False, True, False, ..., True, False, False],
E [ True, True, True, ..., False, False, False],
E ...,
E [ True, False, True, ..., False, False, False],
E [False, False, True, ..., True, False, True],
E [ True, False, False, ..., True, True, False]],
E
E [[False, True, True, ..., False, False, False],
E [False, True, True, ..., True, True, False],
E [ True, True, True, ..., True, False, False],
E ...,
E [False, False, True, ..., False, True, False],
E [False, False, True, ..., True, False, False],
E [ True, True, False, ..., False, True, False]],
E
E ...,
E
E [[ True, True, True, ..., True, False, True],
E [False, True, False, ..., False, True, False],
E [ True, True, True, ..., True, False, True],
E ...,
E [ True, False, False, ..., False, False, True],
E [ True, False, False, ..., True, True, False],
E [False, False, True, ..., True, False, True]],
E
E [[ True, False, True, ..., True, True, True],
E [ True, False, True, ..., False, True, True],
E [ True, False, True, ..., False, False, True],
E ...,
E [False, True, True, ..., True, False, False],
E [ True, True, False, ..., True, False, False],
E [ True, True, False, ..., True, True, True]],
E
E [[False, True, True, ..., True, True, True],
E [ True, False, True, ..., True, True, True],
E [False, True, True, ..., True, False, True],
E ...,
E [False, True, False, ..., True, False, True],
E [ True, True, False, ..., False, True, True],
E [False, True, False, ..., False, False, True]]]]), compiled_model=tensor([[[[0., 0., 0., ..., 1., 0., 1.],
E [0., 1., 0., ..., 0., 1., 0.],
E [0., 0., 1., ..., 0., 1., 1.],
E ...,
E [0., 1., 0., ..., 0., 0., 0.],
E [1., 0., 1., ..., 0., 0., 0.],
E [1., 1., 0., ..., 1., 1., 0.]],
E
E [[1., 0., 0., ..., 0., 0., 1.],
E [0., 1., 1., ..., 0., 1., 1.],
E [1., 1., 0., ..., 0., 1., 1.],
E ...,
E [0., 0., 0., ..., 0., 1., 0.],
E [0., 1., 1., ..., 0., 1., 1.],
E [0., 1., 0., ..., 1., 1., 0.]],
E
E [[0., 1., 1., ..., 1., 0., 0.],
E [0., 1., 0., ..., 1., 0., 1.],
E [1., 0., 1., ..., 0., 0., 1.],
E ...,
E [1., 0., 1., ..., 1., 0., 1.],
E [0., 1., 1., ..., 0., 1., 1.],
E [0., 0., 1., ..., 0., 1., 0.]],
E
E ...,
E
E [[1., 1., 1., ..., 0., 1., 0.],
E [1., 0., 1., ..., 1., 0., 0.],
E [1., 0., 1., ..., 1., 1., 1.],
E ...,
E [1., 1., 1., ..., 1., 0., 0.],
E [1., 1., 1., ..., 1., 0., 1.],
E [1., 1., 1., ..., 1., 0., 1.]],
E
E [[1., 0., 1., ..., 0., 0., 1.],
E [0., 0., 0., ..., 0., 1., 1.],
E [0., 1., 1., ..., 1., 1., 0.],
E ...,
E [0., 1., 0., ..., 0., 0., 1.],
E [1., 0., 1., ..., 0., 0., 0.],
E [1., 0., 0., ..., 1., 1., 1.]],
E
E [[0., 1., 1., ..., 0., 1., 1.],
E [1., 1., 0., ..., 1., 1., 1.],
E [1., 0., 0., ..., 0., 1., 0.],
E ...,
E [1., 0., 1., ..., 0., 1., 0.],
E [0., 1., 0., ..., 0., 0., 0.],
E [1., 1., 0., ..., 0., 0., 1.]]]])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/value_checkers.py:38: ValueError
Check failure on line 110 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_greater[shape_x7-shape_y7]
ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[[False, True, False, ..., False, False, True],
[False, False, False, ..., True, False, True],
[ True, False, False, ..., False, False, False],
...,
[ True, True, True, ..., False, True, True],
[False, False, True, ..., True, False, True],
[ True, False, True, ..., True, False, True]],
[[False, False, False, ..., True, False, False],
[ True, False, True, ..., True, True, True],
[False, False, True, ..., False, False, False],
...,
[ True, True, False, ..., False, True, False],
[ True, False, True, ..., False, True, False],
[ True, False, False, ..., False, True, False]],
[[False, True, False, ..., False, True, True],
[False, False, False, ..., True, True, False],
[ True, False, True, ..., True, False, False],
...,
[False, False, True, ..., False, False, True],
[ True, True, False, ..., False, True, False],
[False, True, False, ..., True, False, True]],
...,
[[ True, False, True, ..., False, True, False],
[False, False, True, ..., True, False, False],
[False, True, False, ..., True, False, True],
...,
[ True, True, True, ..., False, True, True],
[False, False, True, ..., True, False, True],
[ True, True, False, ..., True, False, False]],
[[ True, True, False, ..., True, False, True],
[ True, False, True, ..., False, False, False],
[False, False, False, ..., True, False, True],
...,
[False, False, True, ..., False, True, True],
[ True, True, False, ..., True, False, False],
[ True, False, False, ..., True, True, True]],
[[False, True, True, ..., True, True, True],
[ True, True, True, ..., True, False, False],
[ True, False, False, ..., False, True, False],
...,
[ True, True, False, ..., True, False, False],
[ True, True, True, ..., False, True, True],
[ True, True, True, ..., True, False, False]]]]), compiled_model=tensor([[[[0., 1., 0., ..., 0., 1., 1.],
[1., 0., 0., ..., 0., 1., 1.],
[0., 0., 1., ..., 1., 0., 1.],
...,
[0., 0., 0., ..., 1., 0., 1.],
[0., 1., 1., ..., 0., 1., 1.],
[0., 1., 0., ..., 1., 0., 1.]],
[[0., 0., 0., ..., 1., 1., 0.],
[0., 0., 1., ..., 1., 1., 0.],
[0., 0., 1., ..., 0., 1., 1.],
...,
[0., 0., 1., ..., 1., 0., 1.],
[1., 1., 0., ..., 0., 1., 0.],
[0., 0., 1., ..., 0., 1., 0.]],
[[0., 1., 0., ..., 0., 1., 1.],
[1., 0., 1., ..., 0., 0., 1.],
[1., 1., 0., ..., 0., 0., 1.],
...,
[1., 0., 1., ..., 1., 1., 1.],
[0., 0., 0., ..., 0., 0., 1.],
[1., 0., 1., ..., 1., 0., 1.]],
...,
[[1., 0., 1., ..., 0., 0., 1.],
[0., 1., 0., ..., 0., 0., 0.],
[1., 1., 1., ..., 0., 1., 0.],
...,
[0., 0., 1., ..., 0., 1., 0.],
[1., 1., 1., ..., 0., 1., 1.],
[1., 1., 0., ..., 1., 0., 0.]],
[[1., 1., 0., ..., 1., 1., 1.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 1., 1., ..., 0., 0., 0.],
...,
[1., 0., 0., ..., 1., 1., 1.],
[0., 0., 1., ..., 0., 1., 1.],
[0., 0., 1., ..., 1., 1., 1.]],
[[0., 1., 1., ..., 0., 0., 0.],
[1., 0., 0., ..., 1., 1., 0.],
[1., 0., 1., ..., 1., 1., 1.],
...,
[0., 1., 1., ..., 1., 1., 1.],
[1., 1., 0., ..., 1., 0., 0.],
[0., 1., 1., ..., 1., 0., 0.]]]])Raw output
shape_x = (1, 32, 32, 32), shape_y = (1, 32, 32, 32)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_greater(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)
inputs = [x, y]
framework_model = Greater()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:110:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:333: in verify
verify_cfg.value_checker.check(fw, co)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.verify.value_checkers.AutomaticValueChecker object at 0x7fb619fc4580>
fw_out = tensor([[[[False, True, False, ..., False, False, True],
[False, False, False, ..., True, False, True]... [ True, True, True, ..., False, True, True],
[ True, True, True, ..., True, False, False]]]])
co_out = tensor([[[[0., 1., 0., ..., 0., 1., 1.],
[1., 0., 0., ..., 0., 1., 1.],
[0., 0., 1., ..., 1., 0... [0., 1., 1., ..., 1., 1., 1.],
[1., 1., 0., ..., 1., 0., 0.],
[0., 1., 1., ..., 1., 0., 0.]]]])
def check(self, fw_out, co_out):
if not compare_with_golden(fw_out, co_out, self.pcc, self.rtol, self.atol, self.dissimilarity_threshold):
> raise ValueError(
f"Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model={fw_out}, compiled_model={co_out}"
)
E ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[[False, True, False, ..., False, False, True],
E [False, False, False, ..., True, False, True],
E [ True, False, False, ..., False, False, False],
E ...,
E [ True, True, True, ..., False, True, True],
E [False, False, True, ..., True, False, True],
E [ True, False, True, ..., True, False, True]],
E
E [[False, False, False, ..., True, False, False],
E [ True, False, True, ..., True, True, True],
E [False, False, True, ..., False, False, False],
E ...,
E [ True, True, False, ..., False, True, False],
E [ True, False, True, ..., False, True, False],
E [ True, False, False, ..., False, True, False]],
E
E [[False, True, False, ..., False, True, True],
E [False, False, False, ..., True, True, False],
E [ True, False, True, ..., True, False, False],
E ...,
E [False, False, True, ..., False, False, True],
E [ True, True, False, ..., False, True, False],
E [False, True, False, ..., True, False, True]],
E
E ...,
E
E [[ True, False, True, ..., False, True, False],
E [False, False, True, ..., True, False, False],
E [False, True, False, ..., True, False, True],
E ...,
E [ True, True, True, ..., False, True, True],
E [False, False, True, ..., True, False, True],
E [ True, True, False, ..., True, False, False]],
E
E [[ True, True, False, ..., True, False, True],
E [ True, False, True, ..., False, False, False],
E [False, False, False, ..., True, False, True],
E ...,
E [False, False, True, ..., False, True, True],
E [ True, True, False, ..., True, False, False],
E [ True, False, False, ..., True, True, True]],
E
E [[False, True, True, ..., True, True, True],
E [ True, True, True, ..., True, False, False],
E [ True, False, False, ..., False, True, False],
E ...,
E [ True, True, False, ..., True, False, False],
E [ True, True, True, ..., False, True, True],
E [ True, True, True, ..., True, False, False]]]]), compiled_model=tensor([[[[0., 1., 0., ..., 0., 1., 1.],
E [1., 0., 0., ..., 0., 1., 1.],
E [0., 0., 1., ..., 1., 0., 1.],
E ...,
E [0., 0., 0., ..., 1., 0., 1.],
E [0., 1., 1., ..., 0., 1., 1.],
E [0., 1., 0., ..., 1., 0., 1.]],
E
E [[0., 0., 0., ..., 1., 1., 0.],
E [0., 0., 1., ..., 1., 1., 0.],
E [0., 0., 1., ..., 0., 1., 1.],
E ...,
E [0., 0., 1., ..., 1., 0., 1.],
E [1., 1., 0., ..., 0., 1., 0.],
E [0., 0., 1., ..., 0., 1., 0.]],
E
E [[0., 1., 0., ..., 0., 1., 1.],
E [1., 0., 1., ..., 0., 0., 1.],
E [1., 1., 0., ..., 0., 0., 1.],
E ...,
E [1., 0., 1., ..., 1., 1., 1.],
E [0., 0., 0., ..., 0., 0., 1.],
E [1., 0., 1., ..., 1., 0., 1.]],
E
E ...,
E
E [[1., 0., 1., ..., 0., 0., 1.],
E [0., 1., 0., ..., 0., 0., 0.],
E [1., 1., 1., ..., 0., 1., 0.],
E ...,
E [0., 0., 1., ..., 0., 1., 0.],
E [1., 1., 1., ..., 0., 1., 1.],
E [1., 1., 0., ..., 1., 0., 0.]],
E
E [[1., 1., 0., ..., 1., 1., 1.],
E [0., 0., 0., ..., 0., 0., 0.],
E [0., 1., 1., ..., 0., 0., 0.],
E ...,
E [1., 0., 0., ..., 1., 1., 1.],
E [0., 0., 1., ..., 0., 1., 1.],
E [0., 0., 1., ..., 1., 1., 1.]],
E
E [[0., 1., 1., ..., 0., 0., 0.],
E [1., 0., 0., ..., 1., 1., 0.],
E [1., 0., 1., ..., 1., 1., 1.],
E ...,
E [0., 1., 1., ..., 1., 1., 1.],
E [1., 1., 0., ..., 1., 0., 0.],
E [0., 1., 1., ..., 1., 0., 0.]]]])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/value_checkers.py:38: ValueError
Check failure on line 143 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_not_equal[shape_x0-shape_y0]
RuntimeError: Fatal error
Raw output
shape_x = (1, 128, 28, 28), shape_y = (1, 128, 28, 28)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_not_equal(shape_x, shape_y):
class NotEqual(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)
inputs = [x, y]
framework_model = NotEqual()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:143:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c15060>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0...e-01, 6.38527e-01],
[4.80435e-01, 5.63519e-01, 1.80081e-01, ..., 6.29593e-01, 1.39392e-01, 3.02127e-01]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0...e-01, 6.38527e-01],
[4.80435e-01, 5.63519e-01, 1.80081e-01, ..., 6.29593e-01, 1.39392e-01, 3.02127e-01]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 143 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_not_equal[shape_x2-shape_y2]
RuntimeError: Fatal error
Raw output
shape_x = (1, 256, 28, 28), shape_y = (1, 256, 28, 28)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_not_equal(shape_x, shape_y):
class NotEqual(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)
inputs = [x, y]
framework_model = NotEqual()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:143:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b094c8b0>
inputs = (tensor([[[[4.96257e-01, 7.68222e-01, 8.84774e-02, ..., 5.52907e-01, 9.52738e-01, 3.61648e-02],
[1.85231e-0... 0.67269, ..., 0.52853, 0.02791, 0.72723],
[0.50627, 0.20671, 0.06682, ..., 0.64299, 0.17580, 0.76558]]]]))
inputs_and_parameters = [tensor([[[[4.96257e-01, 7.68222e-01, 8.84774e-02, ..., 5.52907e-01, 9.52738e-01, 3.61648e-02],
[1.85231e-0... 0.67269, ..., 0.52853, 0.02791, 0.72723],
[0.50627, 0.20671, 0.06682, ..., 0.64299, 0.17580, 0.76558]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 143 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_not_equal[shape_x4-shape_y4]
RuntimeError: Fatal error
Raw output
shape_x = (1, 128, 56, 56), shape_y = (1, 128, 56, 56)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_not_equal(shape_x, shape_y):
class NotEqual(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)
inputs = [x, y]
framework_model = NotEqual()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:143:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2cbace0>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0... 0.24537, ..., 0.20951, 0.17554, 0.22976],
[0.29514, 0.97587, 0.57722, ..., 0.83887, 0.36270, 0.54569]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0... 0.24537, ..., 0.20951, 0.17554, 0.22976],
[0.29514, 0.97587, 0.57722, ..., 0.83887, 0.36270, 0.54569]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 143 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_not_equal[shape_x6-shape_y6]
RuntimeError: Fatal error
Raw output
shape_x = (1, 512, 7, 7), shape_y = (1, 512, 7, 7)
@pytest.mark.parametrize(
"shape_x, shape_y",
[
((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.push
def test_not_equal(shape_x, shape_y):
class NotEqual(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)
inputs = [x, y]
framework_model = NotEqual()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:143:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2dae860>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0... 0.53460, ..., 0.32252, 0.38427, 0.26611],
[0.86218, 0.91274, 0.66447, ..., 0.31686, 0.59966, 0.20105]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0... 0.53460, ..., 0.32252, 0.38427, 0.26611],
[0.86218, 0.91274, 0.66447, ..., 0.31686, 0.59966, 0.20105]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape0]
RuntimeError: Fatal error
Raw output
shape = (1, 128, 28, 28)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2b00b50>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0... 0.67392, ..., 0.15695, 1.33041, 1.49866],
[0.76830, 0.61201, 0.12826, ..., 1.79951, 1.97955, 0.70544]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0... 0.67392, ..., 0.15695, 1.33041, 1.49866],
[0.76830, 0.61201, 0.12826, ..., 1.79951, 1.97955, 0.70544]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape2]
RuntimeError: Fatal error
Raw output
shape = (1, 256, 28, 28)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b0902d70>
inputs = (tensor([[[[4.96257e-01, 7.68222e-01, 8.84774e-02, ..., 5.52907e-01, 9.52738e-01, 3.61648e-02],
[1.85231e-0...e+00, 1.27705e+00],
[9.60871e-01, 1.12704e+00, 3.60162e-01, ..., 1.25919e+00, 2.78783e-01, 6.04253e-01]]]]))
inputs_and_parameters = [tensor([[[[4.96257e-01, 7.68222e-01, 8.84774e-02, ..., 5.52907e-01, 9.52738e-01, 3.61648e-02],
[1.85231e-0...e+00, 1.27705e+00],
[9.60871e-01, 1.12704e+00, 3.60162e-01, ..., 1.25919e+00, 2.78783e-01, 6.04253e-01]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape4]
RuntimeError: Fatal error
Raw output
shape = (1, 128, 56, 56)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c939a0>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0... 0.51179, ..., 1.10265, 1.07445, 1.95150],
[0.46215, 0.12042, 1.34537, ..., 1.28598, 0.35160, 1.53116]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0... 0.51179, ..., 1.10265, 1.07445, 1.95150],
[0.46215, 0.12042, 1.34537, ..., 1.28598, 0.35160, 1.53116]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape6]
RuntimeError: Fatal error
Raw output
shape = (1, 512, 7, 7)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2cb8670>
inputs = (tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0... 0.23916, ..., 0.06386, 1.28559, 1.67222],
[1.13158, 1.59839, 0.17406, ..., 0.48676, 1.18056, 1.04700]]]]))
inputs_and_parameters = [tensor([[[[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0... 0.23916, ..., 0.06386, 1.28559, 1.67222],
[1.13158, 1.59839, 0.17406, ..., 0.48676, 1.18056, 1.04700]]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape8]
RuntimeError: Fatal error
Raw output
shape = (128, 28, 28)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c5bfa0>
inputs = (tensor([[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0.5...3, 0.67392, ..., 0.15695, 1.33041, 1.49866],
[0.76830, 0.61201, 0.12826, ..., 1.79951, 1.97955, 0.70544]]]))
inputs_and_parameters = [tensor([[[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0.5...3, 0.67392, ..., 0.15695, 1.33041, 1.49866],
[0.76830, 0.61201, 0.12826, ..., 1.79951, 1.97955, 0.70544]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape11]
RuntimeError: Fatal error
Raw output
shape = (128, 14, 14)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c96bc0>
inputs = (tensor([[[0.49626, 0.76822, 0.08848, ..., 0.40172, 0.02233, 0.16886],
[0.29389, 0.51852, 0.69767, ..., 0.5...4, 0.18371, ..., 0.59819, 0.52339, 0.93409],
[1.53317, 1.88780, 0.23916, ..., 0.48676, 1.18056, 1.04700]]]))
inputs_and_parameters = [tensor([[[0.49626, 0.76822, 0.08848, ..., 0.40172, 0.02233, 0.16886],
[0.29389, 0.51852, 0.69767, ..., 0.5...4, 0.18371, ..., 0.59819, 0.52339, 0.93409],
[1.53317, 1.88780, 0.23916, ..., 0.48676, 1.18056, 1.04700]]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape17]
RuntimeError: Fatal error
Raw output
shape = (64, 28)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2beb700>
inputs = (tensor([[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0.567...215, 1.84882, ..., 1.15385, 0.43569, 0.68789],
[1.78722, 0.50906, 0.93987, ..., 1.55895, 1.06794, 1.79717]]))
inputs_and_parameters = [tensor([[0.49626, 0.76822, 0.08848, ..., 0.55291, 0.95274, 0.03616],
[0.18523, 0.37342, 0.30510, ..., 0.567...215, 1.84882, ..., 1.15385, 0.43569, 0.68789],
[1.78722, 0.50906, 0.93987, ..., 1.55895, 1.06794, 1.79717]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape19]
RuntimeError: Fatal error
Raw output
shape = (128, 14)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c59840>
inputs = (tensor([[0.49626, 0.76822, 0.08848, ..., 0.40172, 0.02233, 0.16886],
[0.29389, 0.51852, 0.69767, ..., 0.552...906, 0.93987, ..., 1.84339, 0.45132, 0.68730],
[1.36408, 1.79865, 1.95659, ..., 1.55895, 1.06794, 1.79717]]))
inputs_and_parameters = [tensor([[0.49626, 0.76822, 0.08848, ..., 0.40172, 0.02233, 0.16886],
[0.29389, 0.51852, 0.69767, ..., 0.552...906, 0.93987, ..., 1.84339, 0.45132, 0.68730],
[1.36408, 1.79865, 1.95659, ..., 1.55895, 1.06794, 1.79717]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape20]
RuntimeError: Fatal error
Raw output
shape = (128, 56)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2c90f40>
inputs = (tensor([[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0.575...018, 1.64450, ..., 1.10540, 1.42029, 0.52418],
[1.53309, 1.29671, 1.77202, ..., 0.58573, 1.86955, 1.20645]]))
inputs_and_parameters = [tensor([[0.49626, 0.76822, 0.08848, ..., 0.56755, 0.83524, 0.20560],
[0.59317, 0.11235, 0.15346, ..., 0.575...018, 1.64450, ..., 1.10540, 1.42029, 0.52418],
[1.53309, 1.29671, 1.77202, ..., 0.58573, 1.86955, 1.20645]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 192 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_equal[shape22]
RuntimeError: Fatal error
Raw output
shape = (512, 7)
@pytest.mark.parametrize(
"shape",
[
(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),
(1, 32, 32, 32),
(128, 28, 28),
(64, 28, 28),
(256, 28, 28),
(128, 14, 14),
(128, 56, 56),
(32, 64, 64),
(512, 7, 7),
(32, 32, 32),
(128, 28),
(64, 28),
(256, 28),
(128, 14),
(128, 56),
(32, 64),
(512, 7),
(32, 32),
],
)
@pytest.mark.push
def test_equal(shape):
class Equal(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.eq(x, y)
x = torch.rand(shape)
y = x * 2.0
inputs = [x, y]
framework_model = Equal()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:192:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2a50ee0>
inputs = (tensor([[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0.401...054, 0.72265, ..., 0.27104, 0.14659, 1.98740],
[0.13614, 1.85093, 1.26928, ..., 1.87098, 0.72776, 0.58766]]))
inputs_and_parameters = [tensor([[0.49626, 0.76822, 0.08848, ..., 0.30742, 0.63408, 0.49009],
[0.89644, 0.45563, 0.63231, ..., 0.401...054, 0.72265, ..., 0.27104, 0.14659, 1.98740],
[0.13614, 1.85093, 1.26928, ..., 1.87098, 0.72776, 0.58766]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 209 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_add
ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[0.73496, 1.12383, 0.26487, ..., 0.61673, 0.91219, 1.20019],
[0.48113, 0.43513, 0.23373, ..., 1.51522, 0.79363, 1.01430],
[1.29557, 1.38254, 1.07331, ..., 1.66554, 0.07209, 0.33904],
...,
[1.72863, 1.08533, 1.57207, ..., 0.28559, 1.11634, 1.19057],
[1.12506, 0.98804, 1.76224, ..., 0.88192, 1.23629, 1.08956],
[0.90576, 0.71437, 1.37417, ..., 0.84095, 0.90509, 1.49623]],
[[1.43176, 0.90725, 0.27420, ..., 1.23006, 0.63938, 1.00249],
[1.82137, 1.03566, 1.02211, ..., 0.86952, 1.31769, 1.43452],
[1.11309, 0.68375, 0.71550, ..., 0.87892, 0.68875, 0.59087],
...,
[1.68523, 0.71244, 1.18100, ..., 0.36217, 1.16124, 0.99175],
[1.00374, 0.47009, 1.67520, ..., 1.36567, 1.63941, 1.35224],
[0.85810, 1.59082, 0.37622, ..., 1.30486, 1.44561, 0.35451]]]), compiled_model=tensor([[[0.73496, 1.12383, 0.26487, ..., 0.35614, 1.24644, 1.14843],
[1.29557, 1.38254, 1.07331, ..., 0.57745, 0.58875, 1.16529],
[1.12588, 0.50664, 0.68537, ..., 0.93239, 0.48721, 1.23928],
...,
[1.03377, 0.77774, 1.39789, ..., 1.55659, 0.84347, 1.39830],
[1.40545, 0.85635, 1.53476, ..., 0.28559, 1.11634, 1.19057],
[1.37749, 1.17447, 0.92949, ..., 0.84095, 0.90509, 1.49623]],
[[1.43176, 0.90725, 0.27420, ..., 1.28238, 1.57625, 0.77028],
[1.11309, 0.68375, 0.71550, ..., 1.69911, 0.54624, 0.88222],
[0.38749, 0.82986, 1.27037, ..., 0.34471, 0.90097, 1.21151],
...,
[0.37735, 0.92445, 1.47703, ..., 1.51981, 1.26251, 0.97893],
[0.78997, 1.28204, 0.11506, ..., 0.36217, 1.16124, 0.99175],
[1.07781, 0.75897, 1.22801, ..., 1.30486, 1.44561, 0.35451]]])Raw output
@pytest.mark.push
def test_add():
class Add(nn.Module):
def __init__(self):
super().__init__()
def forward(self, a, b):
return a + b
inputs = [torch.rand(2, 32, 32), torch.rand(2, 32, 32)]
framework_model = Add()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model)
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:209:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:333: in verify
verify_cfg.value_checker.check(fw, co)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.verify.value_checkers.AutomaticValueChecker object at 0x7fb619fc4580>
fw_out = tensor([[[0.73496, 1.12383, 0.26487, ..., 0.61673, 0.91219, 1.20019],
[0.48113, 0.43513, 0.23373, ..., 1.51...09, 1.67520, ..., 1.36567, 1.63941, 1.35224],
[0.85810, 1.59082, 0.37622, ..., 1.30486, 1.44561, 0.35451]]])
co_out = tensor([[[0.73496, 1.12383, 0.26487, ..., 0.35614, 1.24644, 1.14843],
[1.29557, 1.38254, 1.07331, ..., 0.57...04, 0.11506, ..., 0.36217, 1.16124, 0.99175],
[1.07781, 0.75897, 1.22801, ..., 1.30486, 1.44561, 0.35451]]])
def check(self, fw_out, co_out):
if not compare_with_golden(fw_out, co_out, self.pcc, self.rtol, self.atol, self.dissimilarity_threshold):
> raise ValueError(
f"Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model={fw_out}, compiled_model={co_out}"
)
E ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[0.73496, 1.12383, 0.26487, ..., 0.61673, 0.91219, 1.20019],
E [0.48113, 0.43513, 0.23373, ..., 1.51522, 0.79363, 1.01430],
E [1.29557, 1.38254, 1.07331, ..., 1.66554, 0.07209, 0.33904],
E ...,
E [1.72863, 1.08533, 1.57207, ..., 0.28559, 1.11634, 1.19057],
E [1.12506, 0.98804, 1.76224, ..., 0.88192, 1.23629, 1.08956],
E [0.90576, 0.71437, 1.37417, ..., 0.84095, 0.90509, 1.49623]],
E
E [[1.43176, 0.90725, 0.27420, ..., 1.23006, 0.63938, 1.00249],
E [1.82137, 1.03566, 1.02211, ..., 0.86952, 1.31769, 1.43452],
E [1.11309, 0.68375, 0.71550, ..., 0.87892, 0.68875, 0.59087],
E ...,
E [1.68523, 0.71244, 1.18100, ..., 0.36217, 1.16124, 0.99175],
E [1.00374, 0.47009, 1.67520, ..., 1.36567, 1.63941, 1.35224],
E [0.85810, 1.59082, 0.37622, ..., 1.30486, 1.44561, 0.35451]]]), compiled_model=tensor([[[0.73496, 1.12383, 0.26487, ..., 0.35614, 1.24644, 1.14843],
E [1.29557, 1.38254, 1.07331, ..., 0.57745, 0.58875, 1.16529],
E [1.12588, 0.50664, 0.68537, ..., 0.93239, 0.48721, 1.23928],
E ...,
E [1.03377, 0.77774, 1.39789, ..., 1.55659, 0.84347, 1.39830],
E [1.40545, 0.85635, 1.53476, ..., 0.28559, 1.11634, 1.19057],
E [1.37749, 1.17447, 0.92949, ..., 0.84095, 0.90509, 1.49623]],
E
E [[1.43176, 0.90725, 0.27420, ..., 1.28238, 1.57625, 0.77028],
E [1.11309, 0.68375, 0.71550, ..., 1.69911, 0.54624, 0.88222],
E [0.38749, 0.82986, 1.27037, ..., 0.34471, 0.90097, 1.21151],
E ...,
E [0.37735, 0.92445, 1.47703, ..., 1.51981, 1.26251, 0.97893],
E [0.78997, 1.28204, 0.11506, ..., 0.36217, 1.16124, 0.99175],
E [1.07781, 0.75897, 1.22801, ..., 1.30486, 1.44561, 0.35451]]])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/value_checkers.py:38: ValueError
Check failure on line 227 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_greater_equal[dims1]
RuntimeError: Fatal error
Raw output
dims = (6, 33)
@pytest.mark.parametrize("dims", [(1, 32, 64), (6, 33), (4, 16, 17)])
@pytest.mark.push
def test_greater_equal(dims):
class GreaterEqual(nn.Module):
def __init__(self):
super().__init__()
def forward(self, a, b):
return torch.greater_equal(a, b)
inputs = [torch.rand(dims), torch.rand(dims)]
framework_model = GreaterEqual()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model, VerifyConfig(verify_dtype=False))
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:227:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.compiled_graph_state.CompiledModel object at 0x7fb5b2da72e0>
inputs = (tensor([[0.49626, 0.76822, 0.08848, 0.13203, 0.30742, 0.63408, 0.49009, 0.89644, 0.45563, 0.63231, 0.34889, 0.40172, ...0.94547, 0.66541, 0.99984, 0.75933, 0.81082, 0.32500, 0.73991, 0.55745, 0.38059, 0.21815, 0.21944, 0.11526, 0.83567]]))
inputs_and_parameters = [tensor([[0.49626, 0.76822, 0.08848, 0.13203, 0.30742, 0.63408, 0.49009, 0.89644, 0.45563, 0.63231, 0.34889, 0.40172, ...0.94547, 0.66541, 0.99984, 0.75933, 0.81082, 0.32500, 0.73991, 0.55745, 0.38059, 0.21815, 0.21944, 0.11526, 0.83567]])]
def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
"""
Run inference on the compiled model.
Parameters
----------
inputs: [Tensor, ...]
Input tensors
Returns
-------
List[Tensor]
Output tensors
"""
self.inputs = [*to_pt_tensors(inputs)]
inputs_and_parameters = [
*self.inputs,
*self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
*self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
]
assert all(
[isinstance(t, torch.Tensor) for t in inputs_and_parameters]
), "All inputs should be torch tensors by now."
if self.training() and isinstance(self.framework_module, PyTorchModule):
for name, param in self.framework_module.module.named_parameters():
if param.requires_grad:
our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
# NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
# module. This is because we want to be able to optimize the parameters both on the device
# (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
# the parameter value, the other side can see the change.
#
# This could change in the future, but for now ensure that our premise is correct.
assert param is our_tensor
logger.info(
f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
)
> all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E RuntimeError: Fatal error
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:253: RuntimeError
Check failure on line 268 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py
github-actions / TT-Forge-FE Tests
test_eltwise_binary.test_multiply[shape0]
ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[2.30743e-01, 2.13187e-02, 1.87313e-02, ..., 3.22881e-01, 1.84691e-02, 4.23863e-01],
[1.60184e-01, 1.87168e-01, 1.38811e-01, ..., 5.98106e-01, 1.46211e-01, 2.97836e-01],
[3.63389e-01, 8.14409e-02, 1.65667e-01, ..., 9.60350e-02, 3.18001e-04, 5.39877e-02],
...,
[8.22859e-01, 2.26655e-01, 3.01797e-01, ..., 4.67895e-02, 4.29371e-01, 3.21169e-01],
[1.09746e-01, 2.58656e-01, 7.23061e-01, ..., 5.23428e-01, 4.61224e-01, 4.98801e-01],
[2.67098e-01, 2.77467e-01, 1.18235e-01, ..., 1.10228e-01, 1.73405e-01, 2.09750e-01]]]), compiled_model=tensor([[[0.23074, 0.02132, 0.01873, ..., 0.21045, 0.50561, 0.35740],
[0.36339, 0.08144, 0.16567, ..., 0.54940, 0.10816, 0.20594],
[0.02591, 0.04576, 0.35191, ..., 0.22376, 0.03701, 0.30759],
...,
[0.17571, 0.39912, 0.33128, ..., 0.54841, 0.19968, 0.64131],
[0.41272, 0.44021, 0.06422, ..., 0.04679, 0.42937, 0.32117],
[0.58724, 0.55476, 0.00146, ..., 0.11023, 0.17341, 0.20975]]])Raw output
shape = (1, 32, 32)
@pytest.mark.parametrize(
"shape",
[
(1, 32, 32),
(12, 8640),
],
)
@pytest.mark.push
def test_multiply(shape):
class Multiply(nn.Module):
def __init__(self):
super().__init__()
def forward(self, a, b):
return a * b
inputs = [torch.rand(shape), torch.rand(shape)]
framework_model = Multiply()
compiled_model = forge.compile(framework_model, sample_inputs=inputs)
> verify(inputs, framework_model, compiled_model)
forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:268:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:333: in verify
verify_cfg.value_checker.check(fw, co)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <forge.verify.value_checkers.AutomaticValueChecker object at 0x7fb619fc4580>
fw_out = tensor([[[2.30743e-01, 2.13187e-02, 1.87313e-02, ..., 3.22881e-01, 1.84691e-02, 4.23863e-01],
[1.60184e-01, ...224e-01, 4.98801e-01],
[2.67098e-01, 2.77467e-01, 1.18235e-01, ..., 1.10228e-01, 1.73405e-01, 2.09750e-01]]])
co_out = tensor([[[0.23074, 0.02132, 0.01873, ..., 0.21045, 0.50561, 0.35740],
[0.36339, 0.08144, 0.16567, ..., 0.54...21, 0.06422, ..., 0.04679, 0.42937, 0.32117],
[0.58724, 0.55476, 0.00146, ..., 0.11023, 0.17341, 0.20975]]])
def check(self, fw_out, co_out):
if not compare_with_golden(fw_out, co_out, self.pcc, self.rtol, self.atol, self.dissimilarity_threshold):
> raise ValueError(
f"Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model={fw_out}, compiled_model={co_out}"
)
E ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[2.30743e-01, 2.13187e-02, 1.87313e-02, ..., 3.22881e-01, 1.84691e-02, 4.23863e-01],
E [1.60184e-01, 1.87168e-01, 1.38811e-01, ..., 5.98106e-01, 1.46211e-01, 2.97836e-01],
E [3.63389e-01, 8.14409e-02, 1.65667e-01, ..., 9.60350e-02, 3.18001e-04, 5.39877e-02],
E ...,
E [8.22859e-01, 2.26655e-01, 3.01797e-01, ..., 4.67895e-02, 4.29371e-01, 3.21169e-01],
E [1.09746e-01, 2.58656e-01, 7.23061e-01, ..., 5.23428e-01, 4.61224e-01, 4.98801e-01],
E [2.67098e-01, 2.77467e-01, 1.18235e-01, ..., 1.10228e-01, 1.73405e-01, 2.09750e-01]]]), compiled_model=tensor([[[0.23074, 0.02132, 0.01873, ..., 0.21045, 0.50561, 0.35740],
E [0.36339, 0.08144, 0.16567, ..., 0.54940, 0.10816, 0.20594],
E [0.02591, 0.04576, 0.35191, ..., 0.22376, 0.03701, 0.30759],
E ...,
E [0.17571, 0.39912, 0.33128, ..., 0.54841, 0.19968, 0.64131],
E [0.41272, 0.44021, 0.06422, ..., 0.04679, 0.42937, 0.32117],
E [0.58724, 0.55476, 0.00146, ..., 0.11023, 0.17341, 0.20975]]])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/value_checkers.py:38: ValueError
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