ekalda commented on a change in pull request #9457:
URL: https://github.com/apache/tvm/pull/9457#discussion_r750120691
##########
File path: python/tvm/relay/backend/contrib/ethosu/legalize.py
##########
@@ -123,6 +123,109 @@ def __call__(self, *args, **kwargs):
pass
+class StridedSliceRewriter(DFPatternCallback):
+ """This pass brings the strided slice out of the partitioned function"""
+
+ def __init__(self):
+ super().__init__(require_type=True, rewrite_once=True)
+ self.pattern = (wildcard().has_attr({"Composite":
"ethosu.strided_slice"}))(wildcard())
+
+ def callback(
+ self, pre: tvm.relay.Expr, post: tvm.relay.Expr, node_map:
tvm.ir.container.Map
+ ) -> tvm.relay.Expr:
+ slice_input = post.args[0]
+ attrs = post.op.body.attrs
+ begin = attrs.begin
+ end = attrs.end
+ strides = attrs.strides
+ axes = attrs.axes
+ slice_mode = attrs.slice_mode
+ strided_slice = relay.op.strided_slice(
+ slice_input, begin, end, strides=strides, axes=axes,
slice_mode=slice_mode
+ )
+ return strided_slice
+
+
[email protected]_pass(opt_level=1)
+class LegalizeStridedSlice:
+ """This is the pass that wraps StridedSliceRewriter"""
+
+ def transform_module(
+ self, mod: tvm.ir.IRModule, ctx: tvm.ir.transform.PassContext
+ ) -> tvm.ir.IRModule:
+ for global_var, func in mod.functions.items():
+ func = rewrite(StridedSliceRewriter(), func)
+ mod.update_func(global_var, func)
+ return mod
+
+ def __call__(self, *args, **kwargs):
+ pass
+
+
+class ReshapeRewriter(DFPatternCallback):
+ """This pass brings the reshape out of the partitioned function"""
+
+ def __init__(self):
+ super().__init__(require_type=True, rewrite_once=True)
+ self.pattern = (wildcard().has_attr({"Composite":
"ethosu.reshape"}))(wildcard())
+
+ def callback(
+ self, pre: tvm.relay.Expr, post: tvm.relay.Expr, node_map:
tvm.ir.container.Map
+ ) -> tvm.relay.Expr:
+ reshape_input = post.args[0]
+ new_shape = post.op.body.attrs.newshape
+ reshape = relay.op.reshape(reshape_input, newshape=new_shape)
+ return reshape
+
+
[email protected]_pass(opt_level=1)
+class LegalizeReshape:
+ """This is the pass that wraps ReshapeRewriter"""
+
+ def transform_module(
+ self, mod: tvm.ir.IRModule, ctx: tvm.ir.transform.PassContext
+ ) -> tvm.ir.IRModule:
+ for global_var, func in mod.functions.items():
+ func = rewrite(ReshapeRewriter(), func)
+ mod.update_func(global_var, func)
+ return mod
+
+ def __call__(self, *args, **kwargs):
+ pass
+
+
+class NoOpRewriter(DFPatternCallback):
+ """This pass adds and idenity operator to reshape and strided slice to
avoid a no op
Review comment:
Done
##########
File path: python/tvm/relay/backend/contrib/ethosu/te/identity.py
##########
@@ -0,0 +1,73 @@
+# 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.
+# pylint: disable=invalid-name,unused-argument
+"""Tensor Expression for identity"""
+from tvm import te
+from .dma import read_compute, write_compute
+
+
+def identity_compute(
+ ifm: te.Tensor,
+ lut: te.Tensor,
+ ifm_scale: float,
+ ifm_zero_point: int,
+ ofm_scale: float,
+ ofm_zero_point: int,
+ activation: str,
+) -> te.Tensor:
+ """A compute operator for the NPU identity operator.
+
+ Parameters
+ ----------
+ ifm : te.Tensor
+ The Input Feature Map tensor (IFM).
+ lut : te.Tensor
+ The look-up table values to use if activation is "LUT", "TANH" or
"SIGMOID".
+ ifm_scale : float
+ The quantization scale for the Input Feature Map tensor.
+ ifm_zero_point : int
+ The quantization zero point for the Input Feature Map tensor.
+ ofm_scale : float
+ The quantization scale for the Output Feature Map tensor.
+ ofm_zero_point : int
+ The quantization zero point for the Output Feature Map tensor.
+ activation : str
+ The activation function to use.
+ "NONE" - no activation function.
+ "TANH" - tanh activation function.
+ "SIGMOID" - sigmoid activation function.
+ "LUT" - use a look-up table to perform the activation function.
+
+ Returns
+ -------
+ te.Tensor
+ The OFM tensor.
Review comment:
Done
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