The general strategy is there is a new module, torch.onnx.symbolic, which
contains a function for every ATen method name with the ONNX translation.
While implementing this, I took the opportunity to expunge all references
of 'g' from the public API; instead, it is managed by a global variable in
torch.onnx which tracks the "current graph".
Other changes:
- If you pass a Tensor to op as an argument, it will now automatically be
converted into a Constant ONNX node. This lets us remove needing to
implement ONNX
- Rename value to other, wherever there is both a Scalar and Tensor overload.
This way, keyword dispatch can work uniformly in both cases.
- Deleted any autograd Function classes that both had a symbolic and were ported
to the new C++ autograd implementation. There may still be some straggling
classes that didn't have symbolic.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
- Conv no longer supports bias, so we create an explicit broadcasted
addition afterwards. There is one minor problem, however, which is that
ConvTranspose in Caffe2 has mandatory bias. So there's a hack.
See Note [Caffe2ConvTranspose] for the details.
- Squeeze: dims -> axes
- Transpose: axes -> perm
- Reshape lost its extra output (yay!)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
This is a case of two wrongs make a right. There were a pair of
related bugs;
- We incorrectly translated Transpose as if it were a Permute;
but Torch transpose actually is a *swap* between dimensions.
- Why didn't we ever notice it? In all of our tests, a transpose
was *solely* done to get a weight matrix into the correct form.
But Caffe2's FC operator *implicitly* does a transpose on
the weight matrix.
This commit fixes both of these problems.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
This adds the PyTorch API user documentation for Toffee.
To make the example work, I also converted all "inplace"
ops to export out-of-place in Toffee.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
- BC BREAKING: export now also takes a mandatory file-ish argument, specifying
the file to export the protobuf to. I rewrote the tests to use BytesIO to
get out the string so they could parse it again.
- BC BREAKING: export no longer returns the tensors that were computed. To
get these, use the internal _export function.
- Multiple inputs to models are now supported by passing a tuple to input.
(Old API of a single Variable still works.)
- Keyword arguments to models are now supported via kwargs keyword arg.
- Renamed embed_params to export_params, and it now defaults to True.
- Toffee tests now live in their own test_toffee.py file. I had to
rename a pile of expect files for this.
- Removed defunct torch.toffee imports from autograd to solve module import
cycle.
- Helper function _with_file_like to abstract over opening file-ish arguments,
taken from torch.save()
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
previously:
PythonOp/CppOp Graph -> ToffeeIR, primspecs worked with protobufs
now:
PythonOp/CppOp --ToToffeIR--> jit::Graph of in-memory ToffeIR -> protobufs of ToffeIR
This commit let's primspec functions work directly with JIT IR nodes,
which makes it possible to do a lot more stuff in those functions.
