Effectively, this mode works by compiling op by op as the NN is eagerly executed by PyTorch. Entailed in that compilation is building a representation of the op that can be `torch.jit.script`ed, importing using `ModuleBuilder`, and then executing (e.g., with `RefBackendLinalgOnTensorsBackend`). This mode includes a fallback to conventional PyTorch if anything in the torch-mlir compilation process fails (e.g., unsupported op).
Currently, all e2e tests pass execpt for two that involve an upstream PyTorch bug (https://github.com/pytorch/pytorch/issues/74400).
High priority next steps:
1. A compile cache in order to speed up reruns of the same NN.
2. Integration with IREE (though not in this repo).
3. Integration with `torch.distributed`.
- This commit adds E2E support for `aten.rand_like` and
`aten.bernoulli_.Tensor` ops.
- The `aten.bernoulli(x)` was implemented as:
`aten.bernoulli(x) = rand_like(x) < 0.5`, assuming 0.5 as default
probability, whereas according to the pytorch documentation:
https://pytorch.org/docs/stable/generated/torch.bernoulli.html#torch.bernoulli
the input x in `aten.bernoulli(x)` is itself a tensor containing
probabilities to be used for drawing the binary random number.
- So this commit fixes the `aten.bernoulli(x)` implementation as:
`aten.bernoulli(x) = rand_like(x) < x`.
- It also fixes the case where the input to `aten.bernoulli_.float` is
an integer tensor. In this case the input must be casted to float type
before passing it as operand to `aten.rand_like` op.
`aten.bernoulli_.float(x, p) = rand_like(float(x)) < p`.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Some of the lowerings use the result type obtained from the op itself
to tell the `linalg::GenericOp` what the type of the result should be
rather than using the type of the result tensor given to the
`linalg::GenericOp`. This becomes a problem when the result type of
the op has static size information and the result tensor used in
`linalg::GenericOp` has dynamic dimensions, for `linalg::GenericOp`
expects the result type to be equal to the type of the output tensor.
This commit replaces the use of the result type from the op itself
with the type of the result tensor passed to `linalg::GenericOp`.
In order to not create too many dynamic/static versions of the same
e2e test, e2e tests have only been added to the ops that currently
fail when used with static sizes.
This PR include the following pieces:
- Add torch `Generator` type. `Generator` type is converted to i64 in
refbackend type converter.
- Add seed managment support for the default global generator.
`torch_c.getNextSeed` op is used to get the seed. On refbackend, the
`torch_c.getNextSeed` is lowered to load/store from [0] of global
variable `default_generator` memref<i64> in `InsertRngGlobals` pass.
- Add `aten.uniform_` and testing as an example op for RNG ops. Add
`torch.pseudo.aten.uniform` op. It has the same operands and return as
the `aten.uniform_` from the op registry except for value semantics.
max
Gaurav Shukla
Ramiro Leal-Cavazos
Prashant Kumar
Yi Zhang