Bohan Hou
bb6f8aec55
This PR batches several post-bringup TIRx follow-ups, rebased onto current `main`. ### Changes - **op-dispatch**: per-call exec scope via `Tx.<scope>.op`; remove `ExecScopeStmt` - **namespaces**: split TIRx op namespaces; remove tile-primitive kind attrs - **codegen**: support explicit CUDA launch bounds - **gemm-async**: support contiguous-axis (K-major) operand slicing - **backend reorg**: move in-tree GPU backends out of core into `src/backend/<target>/` and `python/tvm/backend/<target>/` (codegen/runtime/op), with the corresponding `CMakeLists.txt` / `cmake/modules` and include-path updates ### Testing - Builds with `USE_CUDA=ON` / `USE_LLVM=ON` - The TIRx Python test suite (`tests/python/tirx/`) passes locally
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Trainium TVMScript namespaces."""
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from __future__ import annotations
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from collections.abc import Callable
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from typing import Any
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from . import op as _trn_op
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OpWrapper = Callable[[Callable[..., Any]], Callable[..., Any]]
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def _default_op_wrapper() -> OpWrapper:
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from tvm.tirx.script.builder.ir import _op_wrapper # pylint: disable=import-outside-toplevel
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return _op_wrapper
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class NKINamespace:
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"""The NKI instructions submodule."""
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def __init__(self, op_wrapper: OpWrapper | None = None):
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wrap = op_wrapper or _default_op_wrapper()
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self.load = wrap(_trn_op.nki_load)
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self.store = wrap(_trn_op.nki_store)
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self.tensor_copy = wrap(_trn_op.nki_tensor_copy)
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self.matmul = wrap(_trn_op.nki_matmul)
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self.activation = wrap(_trn_op.nki_activation)
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self.activation_reduce = wrap(_trn_op.nki_activation_reduce)
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self.reciprocal = wrap(_trn_op.nki_reciprocal)
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self.tensorreduce = wrap(_trn_op.nki_tensorreduce)
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self.tensortensor = wrap(_trn_op.nki_tensortensor)
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self.tensorscalar = wrap(_trn_op.nki_tensorscalar)
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self.tensorscalar_reduce = wrap(_trn_op.nki_tensorscalar_reduce)
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self.scalar_tensor_tensor = wrap(_trn_op.nki_scalar_tensor_tensor)
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self.scalar_tensor_scalar = wrap(_trn_op.nki_scalar_tensor_scalar)
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self.memset = wrap(_trn_op.nki_memset)
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self.identity = wrap(_trn_op.nki_identity)
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self.affine_select = wrap(_trn_op.nki_affine_select)
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__all__ = ["NKINamespace", "OpWrapper"]
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