Operator support for F.one_hot
#29
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body: '## 🐛 Bug\r\n' +
'\r\n' +
'`thunder` fails When attempting to compile a graph containing `torch.nn.functional.one_hot` within the forward pass.\r\n' +
'The error message indicates that the input to the method must be a `Tensor`, but a `TensorProxy` is received instead.\r\n' +
'\r\n' +
'### To Reproduce\r\n' +
'\r\n' +
'Steps to reproduce the behavior:\r\n' +
'\r\n' +
'- Define a PyTorch model class with a forward pass involving `F.one_hot` to convert the input tensor to a one-hot encoded representation.\r\n' +
'- Create an instance of the model and evaluate it on a random input tensor.\r\n' +
'- Compile the model using `thunder.jit`.\r\n' +
'- Call the compiled model with the same input tensor.\r\n' +
'\r\n' +
'#### Example\r\n' +
'\r\n' +
'```python\r\n' +
'import thunder\r\n' +
'\r\n' +
'\r\n' +
'class MLP(nn.Module):\r\n' +
' def __init__(self, hidden_size=1024):\r\n' +
' super(MLP, self).__init__()\r\n' +
' self.hidden = nn.Linear(6 * 256, hidden_size, bias=False)\r\n' +
' self.head = nn.Linear(hidden_size, 32000, bias=False)\r\n' +
'\r\n' +
' def forward(self, inputs):\r\n' +
' x = F.one_hot(inputs, 6).reshape(-1, 6 * 256).float()\r\n' +
' x = self.hidden(x)\r\n' +
' logits = self.head(x)\r\n' +
' return logits\r\n' +
'\r\n' +
'\r\n' +
'x = torch.randint(0, 6, (1, 256))\r\n' +
'\r\n' +
'model = MLP(1024).eval()\r\n' +
'print(model(x))\r\n' +
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body: '## 🐛 Bug\r\n' +
'\r\n' +
'`thunder` fails When attempting to compile a graph containing `torch.nn.functional.one_hot` within the forward pass.\r\n' +
'The error message indicates that the input to the method must be a `Tensor`, but a `TensorProxy` is received instead.\r\n' +
'\r\n' +
'### To Reproduce\r\n' +
'\r\n' +
'Steps to reproduce the behavior:\r\n' +
'\r\n' +
'- Define a PyTorch model class with a forward pass involving `F.one_hot` to convert the input tensor to a one-hot encoded representation.\r\n' +
'- Create an instance of the model and evaluate it on a random input tensor.\r\n' +
'- Compile the model using `thunder.jit`.\r\n' +
'- Call the compiled model with the same input tensor.\r\n' +
'\r\n' +
'#### Example\r\n' +
'\r\n' +
'```python\r\n' +
'import thunder\r\n' +
'\r\n' +
'\r\n' +
'class MLP(nn.Module):\r\n' +
' def __init__(self, hidden_size=1024):\r\n' +
' super(MLP, self).__init__()\r\n' +
' self.hidden = nn.Linear(6 * 256, hidden_size, bias=False)\r\n' +
' self.head = nn.Linear(hidden_size, 32000, bias=False)\r\n' +
'\r\n' +
' def forward(self, inputs):\r\n' +
' x = F.one_hot(inputs, 6).reshape(-1, 6 * 256).float()\r\n' +
' x = self.hidden(x)\r\n' +
' logits = self.head(x)\r\n' +
' return logits\r\n' +
'\r\n' +
'\r\n' +
'x = torch.randint(0, 6, (1, 256))\r\n' +
'\r\n' +
'model = MLP(1024).eval()\r\n' +
'print(model(x))\r\n' +
'\r\n' +
'model = thunder.jit(model)\r\n' +
'print(model(x))\r\n' +
'```\r\n' +
'\r\n' +
'<details>\r\n' +
'<summary>Output</summary>\r\n' +
'\r\n' +
'```\r\n' +
'tensor([[-0.1134, -0.0827, -0.0205, ..., 0.0757, 0.0066, 0.0974]],\r\n' +
' grad_fn=<MmBackward0>)\r\n' +
'---------------------------------------------------------------------------\r\n' +
'TypeError Traceback (most recent call last)\r\n' +
'[<ipython-input-6-6425e5faad6e>](https://localhost:8080/#) in <cell line: 23>()\r\n' +
' 21 \r\n' +
' 22 model = thunder.jit(model)\r\n' +
'---> 23 print(model(x))\r\n' +
'\r\n' +
'16 frames\r\n' +
'[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _wrapped_call_impl(self, *args, **kwargs)\r\n' +
' 1509 # type ignore was added because at this point one knows that\r\n' +
' 1510 # torch.jit._trace._trace_module_map is not Optional and has type Dict[Any, Any]\r\n' +
'-> 1511 name = torch.jit._trace._trace_module_map[self] if self in torch.jit._trace._trace_module_map else None # type: ignore[index, operator] # noqa: B950\r\n' +
' 1512 if name:\r\n' +
' 1513 tracing_state.push_scope(name)\r\n' +
'\r\n' +
'[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in _call_impl(self, *args
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