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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/07/20 15:41:23 UTC
[GitHub] [tvm] lhutton1 commented on a diff in pull request #12087: [UMA] UMA v1.0
lhutton1 commented on code in PR #12087:
URL: https://github.com/apache/tvm/pull/12087#discussion_r924943667
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python/tvm/relay/backend/contrib/uma/api/partitioner.py:
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@@ -0,0 +1,118 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Partitioner base class of the Universal Modular Accelerator Interface (UMA)"""
+
+from typing import Callable, Dict, List, Tuple, Optional
+
+import tvm
+from tvm import relay
+from tvm.relay.build_module import bind_params_by_name
+from tvm.relay.op.contrib.register import register_pattern_table
+from .utils import PassPhase
+
+
+PatternTable = List[Tuple[str, tvm.relay.dataflow_pattern.DFPattern, Callable]]
+
+
+class UMAPartitioner:
+ """Partitioner base class of the Universal Modular Accelerator Interface (UMA)."""
+
+ def __init__(self, target_name: str, merge_compiler_regions: bool = True) -> None:
+ self.target_name = target_name
+ self.merge_compiler_regions = merge_compiler_regions
+
+ self._relay_passes: List[Tuple[PassPhase, tvm.transform.Pass]] = []
+ self._patterns: PatternTable = []
+
+ def add_pattern(
+ self,
+ name: str,
+ pattern: tvm.relay.dataflow_pattern.DFPattern,
+ predicate: Optional[Callable] = None,
+ ) -> None:
+ """Add pattern to UMA partitioner
+
+ Parameters
+ ----------
+ name : str
+ relay name of pattern
+
+ pattern: tvm.relay.dataflow_pattern.DFPattern
+ pattern description as DFPattern
+
+ predicate: Optional[Callable]
+ Optional predicate
+
+ """
+
+ name = self.target_name + "." + name
+ if predicate:
+ self._patterns.append((name, pattern, predicate))
+ else:
+ self._patterns.append((name, pattern))
+
+ def _pattern_table(self) -> PatternTable:
+ return self._patterns
+
+ def register(self) -> None:
+ """Register all relevant relay-to-relay functions."""
+ register_pattern_table(self.target_name, self._pattern_table)
+
+ def partition(
+ self, mod: tvm.IRModule, params: Optional[Dict[str, tvm.runtime.NDArray]] = None
+ ) -> tvm.IRModule:
+ """Partition the relay graph in by the NPU supported and unsupported parts.
+
+ Parameters
+ ----------
+ mod : tvm.IRModule
+ The relay module to be partitioned.
+
+ params: Optional[Dict[str, tvm.runtime.NDArray]]
+
+ Returns
+ -------
+ out : tvm.IRModule
+ The partitioned relay module.
+
+ """
+ if params:
+ mod["main"] = bind_params_by_name(mod["main"], params)
+
+ mod = relay.transform.InferType()(mod)
Review Comment:
I wonder if using a single `Sequential` block might make things a bit clearer here. e.g.
```
seq = []
seq.extend([p[1] for p in self._relay_passes if p[0] == PassPhase.PRE_PARTITIONING])
seq.append(relay.transform.MergeComposite(self._pattern_table()))
....
mod = seq(mod)
```
Further, it'll reduce the need to explicitly run `InferType` and manage other pass dependencies
##########
python/tvm/relay/backend/contrib/uma/api/lower.py:
##########
@@ -0,0 +1,159 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Lowering base class of the Universal Modular Accelerator Interface (UMA)"""
+
+from typing import List, Tuple, Callable, Optional
+
+import tvm
+from tvm import relay, te
+from tvm.relay.op.op import register_strategy
+from . import _ffi_api
+from .utils import PassPhase
+
+
+class UMALower:
+ """Lowering base class of the Universal Modular Accelerator Interface (UMA)."""
+
+ def __init__(self, target_name: str) -> None:
+ self.target_name = target_name
+
+ self._operator_strategies: List[
+ Tuple[
+ str,
+ Callable[
+ [tvm.ir.Attrs, tvm.ir.Array, tvm.ir.TensorType, tvm.target.Target],
+ tvm.relay.op.op.OpStrategy,
+ ],
+ Optional[int],
+ ]
+ ] = []
Review Comment:
Would it make sense to break these type annotations into smaller named variables? e.g. like `PatternTable` in partitioner.py
##########
python/tvm/relay/backend/contrib/uma/backend.py:
##########
@@ -0,0 +1,299 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Backend base class of the Universal Modular Accelerator Interface (UMA)"""
+
+from abc import ABC, abstractmethod
+from typing import Union, Dict, Callable, Optional, Any
+
+import tvm
+from tvm.relay.backend.contrib.uma.api.codegen import UMACodegen
+from tvm.relay.backend.contrib.uma.api.lower import UMALower
+from tvm.relay.backend.contrib.uma.api.partitioner import UMAPartitioner
+from tvm.relay.backend.contrib.uma.api.utils import PassPhase
+
+
+class UMABackend(ABC):
+ """Backend base class of the Universal Modular Accelerator Interface (UMA)"""
+
+ def __init__(self, merge_compiler_regions: bool = True) -> None:
+ self._target_attrs: Dict = {}
+ self._target_preprocessor: Callable[[str], Dict[str, Any]] = None
+ self._relay_to_relay = UMAPartitioner(self.target_name, merge_compiler_regions)
+ self._relay_to_tir = UMALower(self.target_name)
+ self._tir_to_runtime = UMACodegen(self.target_name)
+
+ @property
+ @abstractmethod
+ def target_name(self) -> str:
+ """Name of the hardware target.
+
+ Returns
+ -------
+ out : str
+ The hardware target name.
+ """
+ ...
+
+ ############################################################################
+ # Target configuration
+ ############################################################################
+ def _register_target_attr(
+ self,
+ name: str,
+ default: Optional[Union[str, int, bool]] = "",
+ ) -> None:
+ """Register a target attribute name that can be used during target instantiation.
+ Parameters
+ ----------
+ name: str
+ The name of the target attribute.
+
+ default: Optional[Union[str, int, bool]]
+ A default value for the attribute.
+ If none is provided, the attribute will be treated as a string.
+
+ Example
+ -------
+ Here is an example of how two attribute options are registered.
+
+ .. code-block:: python
+
+ self._register_target_attr("attrA", default=0)
+ self._register_target_attr("attrB", default=False)
+ """
+ self._target_attrs[name] = default
+
+ ############################################################################
+ # Relay to Relay function registration
+ ############################################################################
+ def _register_relay_pass(self, phase: PassPhase, relay_pass: tvm.transform.Pass) -> None:
+ """Registers a relay pass at the given phase in the lowering process.
+
+ Parameters
+ ----------
+ phase: PassPhase
+ The phase at which the pass is registered.
+
+ relay_pass: tvm.transform.Pass
+ The relay pass to be registered.
+
+ Example
+ -------
+ Here is an example of how two relay passes are registered.
+ Passes of the same phase are executed in the order they are registered.
+
+ .. code-block:: python
+
+ self._register_relay_pass(PassPhase.PRE_PARTITIONING, MyPassA)
+ self._register_relay_pass(PassPhase.POST_PARTITIONING, MyPassB)
+
+ Where a relay pass can look like this:
+
+ .. code-block:: python
+
+ @tvm.ir.transform.module_pass(opt_level=0)
+ class MyPassA:
+ def transform_module(self, mod, ctx):
+ # My pass functionality...
+ return mod
+ """
+ self._relay_to_relay._relay_passes.append((phase, relay_pass))
+
+ def _register_pattern(
+ self,
+ name: str,
+ pattern: tvm.relay.dataflow_pattern.DFPattern,
+ predicate: Optional[Callable] = None,
+ ) -> None:
+ """Registers a dataflow pattern that is used to partition the relay graph.
+
+ Parameters
+ ----------
+ name: str
+ The name of the pattern.
+
+ pattern: tvm.relay.dataflow_pattern.DFPattern
+ The dataflow pattern.
+
+ predicate: Callable Receiving the matched pattern and
Review Comment:
nit: missing part of sentence?
##########
tests/scripts/task_python_uma.sh:
##########
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -euxo pipefail
+
+source tests/scripts/setup-pytest-env.sh
+
+run_pytest ctypes test_uma tests/python/contrib/test_uma
+run_pytest cython3 test_uma tests/python/contrib/test_uma
Review Comment:
Curious if there is a need to run these tests separately? I believe they should already get run as part of `task_python_integration` (https://github.com/apache/tvm/blob/main/tests/scripts/task_python_integration.sh#L64)?
##########
tests/python/contrib/test_uma/test_uma_vanilla_accelerator.py:
##########
@@ -0,0 +1,61 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""UMA testcase for the vanilla_accelerator accelerator"""
+import pytest
+
+import tvm
+from tvm import tir
+from tvm.relay.dataflow_pattern import is_op, wildcard
+from tvm.relay.backend.contrib.uma.api.utils import PassPhase
+from tvm.relay.backend.contrib.uma.backend import UMABackend
+from tvm.relay.backend.contrib.uma._template.passes import (
+ MyAiHwConv2dPass as VanillaAcceleratorConv2dPass,
+)
+from tvm.relay.backend.contrib.uma._template.codegen import gen_includes
+
+from tvm.relay.backend.contrib.uma._template.patterns import conv2d_pattern
+
+# def conv2d_pattern():
+# pattern = is_op("nn.conv2d")(wildcard(), wildcard())
+# pattern = pattern.has_attr({"strides": [1, 1]})
+# return pattern
Review Comment:
nit: remove
##########
tests/python/contrib/test_uma/test_partition.py:
##########
@@ -0,0 +1,71 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import pytest
+
Review Comment:
Should the tests be guarded in some way based on whether or not TVM was compiled with UMA support?
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