lhutton1 commented on a change in pull request #9384:
URL: https://github.com/apache/tvm/pull/9384#discussion_r738544686
##########
File path: python/tvm/relay/op/contrib/ethosu.py
##########
@@ -331,6 +332,133 @@ def qnn_depthwise_conv2d_pattern() ->
tvm.relay.dataflow_pattern.DFPattern:
return clip_or_req
+class MaxPool2DParams:
+ """
+ This class will parse a call to a ethosu.maxpool2d composite function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.maxpool2d"
+ # The hardware only supports padding upto the numbers as follows
+ padding_bounds = [127, 127, 128, 128]
+
+ def __init__(self, func_body: Call):
+ clip = None
+ if str(func_body.op) == "clip":
+ clip = func_body
+ pool_op = clip.args[0]
+ else:
+ pool_op = func_body
+
+ attrs = pool_op.attrs
+ self.ifm = TensorParams(pool_op.args[MaxPoolArgs.ifm.value],
attrs.layout)
+ self.ofm = TensorParams(pool_op, attrs.layout)
+ self.pool_shape = [int(i) for i in attrs.pool_size]
Review comment:
Just wondering why `pool_size` is not already a tuple of `ints` here?
See https://github.com/apache/tvm/blob/main/python/tvm/relay/op/nn/nn.py#L852
##########
File path: python/tvm/relay/backend/contrib/ethosu/te/pooling.py
##########
@@ -0,0 +1,134 @@
+# 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 Expressions for poolings"""
+from typing import Tuple
+
+from tvm import te
+from .dma import dma_ofm_compute, dma_ifm_compute
+
+
+def pooling_compute(
+ ifm: te.Tensor,
+ lut: te.Tensor,
+ pooling_type: str,
+ ifm_scale: float,
+ ifm_zero_point: int,
+ ofm_scale: float,
+ ofm_zero_point: int,
+ pool_shape: Tuple[int, int],
+ ofm_channels: int,
+ strides: Tuple[int, int],
+ padding: Tuple[int, int, int, int],
+ activation: str,
+ clip_min: int,
+ clip_max: int,
+ upscale: str,
+ ifm_layout: str,
+ ofm_layout: str,
+) -> te.Tensor:
+ """A compute operator representing the capabilities of pooling for the NPU.
+
+ Parameters
+ ----------
+ ifm : te.Tensor
+ The Input Feature Map tensor (IFM).
+ lut : te.Tensor
+ The look-up table values to use if activation = "LUT".
Review comment:
Same comments as above
##########
File path: src/relay/op/contrib/ethosu/pooling.cc
##########
@@ -0,0 +1,186 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file src/relay/op/contrib/ethosu/pooling.cc
+ * \brief Pooling operators definitions for the Arm(R) Ethos(TM)-U NPU
convolution ops.
+ */
+#include <tvm/relay/op.h>
+
+#include "common.h"
+
+namespace tvm {
+namespace relay {
+namespace op {
+namespace contrib {
+namespace ethosu {
+
+/*! \brief Attributes used by the Ethos(TM)-U NPU pooling operator */
+struct EthosuPoolingAttrs : public tvm::AttrsNode<EthosuPoolingAttrs> {
+ String pooling_type;
+ double ifm_scale;
+ int ifm_zero_point;
+ double ofm_scale;
+ int ofm_zero_point;
+ Array<IndexExpr> pool_shape;
+ IndexExpr ofm_channels;
+ Array<IndexExpr> strides;
+ Array<IndexExpr> padding;
+ String activation;
+ int clip_min;
+ int clip_max;
+ String upscale;
+ String ifm_layout;
+ String ofm_layout;
+
+ TVM_DECLARE_ATTRS(EthosuPoolingAttrs, "relay.attrs.EthosuPoolingAttrs") {
+ TVM_ATTR_FIELD(pooling_type)
+ .describe("The type of the pooling. 'AVG' - average pool, 'MAX' - max
pool.");
+ TVM_ATTR_FIELD(ifm_scale).describe("The quantization scale for the Input
Feature Map tensor.");
+ TVM_ATTR_FIELD(ifm_zero_point)
+ .describe("The quantization zero point for the Input Feature Map
tensor.");
+ TVM_ATTR_FIELD(ofm_scale).describe("The quantization scale for the Output
Feature Map tensor.");
+ TVM_ATTR_FIELD(ofm_zero_point)
+ .describe("The quantization zero point for the Output Feature Map
tensor.");
+ TVM_ATTR_FIELD(pool_shape)
+ .describe("The 2 dimensional pool shape as (pool_shape_height,
pool_shape_width).")
+ .set_default(NullValue<Array<IndexExpr> >());
+ TVM_ATTR_FIELD(ofm_channels)
+ .describe(" The number of OFM channels.")
Review comment:
Same here about OFM
##########
File path: python/tvm/relay/backend/contrib/ethosu/op/pooling.py
##########
@@ -0,0 +1,182 @@
+# 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=unused-argument
+"""Relay operators for pooling"""
+from typing import Tuple
+
+import tvm
+from tvm.relay.op import _make
+from tvm.topi.generic import schedule_injective
+from tvm.relay.op.op import OpStrategy
+from tvm.relay.op import strategy as _strategy
+
+from ..te import pooling_compute
+
+
+def _extract_ethosu_pooling_params(attrs, args):
+ """Get the parameters necessary to construct a ethosu_pooling compute TE
+ from a ethosu_pooling Relay call."""
+ ifm = args[0]
+ lut = args[1]
+ pooling_type = attrs.pooling_type
+ ifm_scale = attrs.ifm_scale
+ ifm_zero_point = attrs.ifm_zero_point
+ ofm_scale = attrs.ofm_scale
+ ofm_zero_point = attrs.ofm_zero_point
+ pool_shape = attrs.pool_shape
+ ofm_channels = attrs.ofm_channels
+ strides = attrs.strides
+ padding = attrs.padding
+ activation = attrs.activation
+ clip_min = attrs.clip_min
+ clip_max = attrs.clip_max
+ upscale = attrs.upscale
+ ifm_layout = attrs.ifm_layout
+ ofm_layout = attrs.ofm_layout
+
+ return (
+ ifm,
+ lut,
+ pooling_type,
+ ifm_scale,
+ ifm_zero_point,
+ ofm_scale,
+ ofm_zero_point,
+ pool_shape,
+ ofm_channels,
+ strides,
+ padding,
+ activation,
+ clip_min,
+ clip_max,
+ upscale,
+ ifm_layout,
+ ofm_layout,
+ )
+
+
[email protected]_op_attr("contrib.ethosu.pooling", "FTVMCompute")
+def create_ethosu_pooling_compute(attrs, args, out_type):
+ """Create an ethosu_pooling compute op."""
+ params = _extract_ethosu_pooling_params(attrs, args)
+ op = pooling_compute(*params)
+ return [op]
+
+
[email protected]_op_attr("contrib.ethosu.pooling", "FTVMStrategy")
+def pooling_strategy_ethosu(attrs, inputs, out_type, target):
+ strategy = OpStrategy()
+ strategy.add_implementation(
+ create_ethosu_pooling_compute,
+ _strategy.wrap_topi_schedule(schedule_injective),
+ name="ethosu_pooling",
+ )
+ return strategy
+
+
+def ethosu_pooling(
+ ifm: tvm.relay.Expr,
+ lut: tvm.relay.Expr,
+ pooling_type: str,
+ ifm_scale: float,
+ ifm_zero_point: int,
+ ofm_scale: float,
+ ofm_zero_point: int,
+ pool_shape: Tuple[int, int],
+ ofm_channels: int,
+ strides: Tuple[int, int] = (1, 1),
+ padding: Tuple[int, int, int, int] = (0, 0, 0, 0),
+ activation: str = "NONE",
+ clip_min: int = 0,
+ clip_max: int = 0,
+ upscale: str = "NONE",
+ ifm_layout: str = "NHWC",
+ ofm_layout: str = "NHWC",
+) -> tvm.relay.Call:
+ """This is a quantized 2D pooling operation as supported by the
+ Ethos(TM)-U NPU. It accepts either NHWC or NHCWB16 format
+ for the input data.
+
+ Parameters
+ ----------
+ ifm : tvm.relay.Expr
+ The Input Feature Map tensor (IFM).
+ lut : tvm.relay.Expr
+ The look-up table values to use if activation = "LUT".
Review comment:
Nit: table "of" values
##########
File path: python/tvm/relay/backend/contrib/ethosu/op/pooling.py
##########
@@ -0,0 +1,182 @@
+# 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=unused-argument
+"""Relay operators for pooling"""
+from typing import Tuple
+
+import tvm
+from tvm.relay.op import _make
+from tvm.topi.generic import schedule_injective
+from tvm.relay.op.op import OpStrategy
+from tvm.relay.op import strategy as _strategy
+
+from ..te import pooling_compute
+
+
+def _extract_ethosu_pooling_params(attrs, args):
+ """Get the parameters necessary to construct a ethosu_pooling compute TE
+ from a ethosu_pooling Relay call."""
+ ifm = args[0]
+ lut = args[1]
+ pooling_type = attrs.pooling_type
+ ifm_scale = attrs.ifm_scale
+ ifm_zero_point = attrs.ifm_zero_point
+ ofm_scale = attrs.ofm_scale
+ ofm_zero_point = attrs.ofm_zero_point
+ pool_shape = attrs.pool_shape
+ ofm_channels = attrs.ofm_channels
+ strides = attrs.strides
+ padding = attrs.padding
+ activation = attrs.activation
+ clip_min = attrs.clip_min
+ clip_max = attrs.clip_max
+ upscale = attrs.upscale
+ ifm_layout = attrs.ifm_layout
+ ofm_layout = attrs.ofm_layout
+
+ return (
+ ifm,
+ lut,
+ pooling_type,
+ ifm_scale,
+ ifm_zero_point,
+ ofm_scale,
+ ofm_zero_point,
+ pool_shape,
+ ofm_channels,
+ strides,
+ padding,
+ activation,
+ clip_min,
+ clip_max,
+ upscale,
+ ifm_layout,
+ ofm_layout,
+ )
+
+
[email protected]_op_attr("contrib.ethosu.pooling", "FTVMCompute")
+def create_ethosu_pooling_compute(attrs, args, out_type):
+ """Create an ethosu_pooling compute op."""
+ params = _extract_ethosu_pooling_params(attrs, args)
+ op = pooling_compute(*params)
+ return [op]
+
+
[email protected]_op_attr("contrib.ethosu.pooling", "FTVMStrategy")
+def pooling_strategy_ethosu(attrs, inputs, out_type, target):
+ strategy = OpStrategy()
+ strategy.add_implementation(
+ create_ethosu_pooling_compute,
+ _strategy.wrap_topi_schedule(schedule_injective),
+ name="ethosu_pooling",
+ )
+ return strategy
+
+
+def ethosu_pooling(
+ ifm: tvm.relay.Expr,
+ lut: tvm.relay.Expr,
+ pooling_type: str,
+ ifm_scale: float,
+ ifm_zero_point: int,
+ ofm_scale: float,
+ ofm_zero_point: int,
+ pool_shape: Tuple[int, int],
+ ofm_channels: int,
+ strides: Tuple[int, int] = (1, 1),
+ padding: Tuple[int, int, int, int] = (0, 0, 0, 0),
+ activation: str = "NONE",
+ clip_min: int = 0,
+ clip_max: int = 0,
+ upscale: str = "NONE",
+ ifm_layout: str = "NHWC",
+ ofm_layout: str = "NHWC",
+) -> tvm.relay.Call:
+ """This is a quantized 2D pooling operation as supported by the
+ Ethos(TM)-U NPU. It accepts either NHWC or NHCWB16 format
+ for the input data.
+
+ Parameters
+ ----------
+ ifm : tvm.relay.Expr
+ The Input Feature Map tensor (IFM).
+ lut : tvm.relay.Expr
+ The look-up table values to use if activation = "LUT".
+ pooling_type: str
+ The type of the pooling. "AVG" - average pool, "MAX" - max pool.
+ 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.
+ pool_shape : tuple of int
+ The 2 dimensional pool shape as (pool_shape_height, pool_shape_width).
+ ofm_channels : int
+ The number of OFM channels
Review comment:
Output Feature Map -- Since the abbreviation hasn't yet been declared in
this doc string
##########
File path: src/relay/op/contrib/ethosu/pooling.cc
##########
@@ -0,0 +1,186 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file src/relay/op/contrib/ethosu/pooling.cc
+ * \brief Pooling operators definitions for the Arm(R) Ethos(TM)-U NPU
convolution ops.
+ */
+#include <tvm/relay/op.h>
+
+#include "common.h"
+
+namespace tvm {
+namespace relay {
+namespace op {
+namespace contrib {
+namespace ethosu {
+
+/*! \brief Attributes used by the Ethos(TM)-U NPU pooling operator */
+struct EthosuPoolingAttrs : public tvm::AttrsNode<EthosuPoolingAttrs> {
+ String pooling_type;
+ double ifm_scale;
+ int ifm_zero_point;
+ double ofm_scale;
+ int ofm_zero_point;
+ Array<IndexExpr> pool_shape;
+ IndexExpr ofm_channels;
+ Array<IndexExpr> strides;
+ Array<IndexExpr> padding;
+ String activation;
+ int clip_min;
+ int clip_max;
+ String upscale;
+ String ifm_layout;
+ String ofm_layout;
+
+ TVM_DECLARE_ATTRS(EthosuPoolingAttrs, "relay.attrs.EthosuPoolingAttrs") {
+ TVM_ATTR_FIELD(pooling_type)
+ .describe("The type of the pooling. 'AVG' - average pool, 'MAX' - max
pool.");
+ TVM_ATTR_FIELD(ifm_scale).describe("The quantization scale for the Input
Feature Map tensor.");
+ TVM_ATTR_FIELD(ifm_zero_point)
+ .describe("The quantization zero point for the Input Feature Map
tensor.");
+ TVM_ATTR_FIELD(ofm_scale).describe("The quantization scale for the Output
Feature Map tensor.");
+ TVM_ATTR_FIELD(ofm_zero_point)
+ .describe("The quantization zero point for the Output Feature Map
tensor.");
+ TVM_ATTR_FIELD(pool_shape)
+ .describe("The 2 dimensional pool shape as (pool_shape_height,
pool_shape_width).")
+ .set_default(NullValue<Array<IndexExpr> >());
+ TVM_ATTR_FIELD(ofm_channels)
+ .describe(" The number of OFM channels.")
+ .set_default(NullValue<IndexExpr>());
+ TVM_ATTR_FIELD(strides)
+ .set_default(Array<IndexExpr>({1, 1}))
+ .describe("The 2 dimensional strides as (stride_height,
stride_width).");
+ TVM_ATTR_FIELD(padding)
+ .describe("The 4 dimensional padding as (pad_top, pad_left,
pad_bottom, pad_right).")
+ .set_default(Array<IndexExpr>({0, 0, 0, 0}));
+ TVM_ATTR_FIELD(activation)
+ .describe(
+ "The activation function to use. "
+ "'NONE' - no activation function. "
+ "'CLIP' - clip the output between clip_min and clip_max. "
+ "'TANH' - tanh activation function. "
+ "'SIGMOID' - sigmoid activation function. "
+ "'LUT' - use a look-up table to perform the activation function.")
+ .set_default("NONE");
+ TVM_ATTR_FIELD(clip_min)
+ .describe("The minimum clipping value if activation = 'CLIP'.")
+ .set_default(0);
+ TVM_ATTR_FIELD(clip_max)
+ .describe("The maximum clipping value if activation = 'CLIP'.")
+ .set_default(0);
+ TVM_ATTR_FIELD(upscale)
+ .describe(
+ "The 2x2 upscaling mode to apply to the Input Feature Map tensor. "
+ "'NONE' - no upscaling. "
+ "'NEAREST' - upscale using nearest neighbour. "
+ "'ZEROS' - upscale using zeros.")
+ .set_default("NONE");
+ TVM_ATTR_FIELD(ifm_layout)
+ .describe("The layout of the Input Feature Map tensor. Can be 'NHWC'
or 'NHCWB16'.")
+ .set_default("NHWC");
+ TVM_ATTR_FIELD(ofm_layout)
+ .describe("The layout of the Output Feature Map tensor. Can be 'NHWC'
or 'NHCWB16'.")
+ .set_default("NHWC");
+ }
+};
+
+TVM_REGISTER_NODE_TYPE(EthosuPoolingAttrs);
+
+bool EthosuPoolingRel(const Array<Type>& types, int num_inputs, const Attrs&
attrs,
+ const TypeReporter& reporter) {
+ int ifm_index = 0;
+ int result_index = 2;
+ ICHECK_EQ(types.size(), result_index + 1);
+
+ const auto* ifm = types[ifm_index].as<TensorTypeNode>();
+ if (ifm == nullptr) return false;
+
+ const auto* param = attrs.as<EthosuPoolingAttrs>();
+ ICHECK(param != nullptr) << "EthosuPoolingAttrs cannot be nullptr.";
+
+ bool is_avg_pooling = param->pooling_type == "AVG";
+ ICHECK(is_avg_pooling || param->pooling_type == "MAX")
+ << "Expected pooling_type 'AVG' or 'MAX' but was" << param->pooling_type;
+
+ ICHECK(ifm->dtype == DataType::UInt(8) || ifm->dtype == DataType::Int(8))
+ << "Expected pool type(uint8) or type(int8) for ifm but was " <<
ifm->dtype;
+
+ // Assign ofm type
+ auto ofm_shape = EthosuInferKernelOutput(
+ ifm->shape, param->ifm_layout, param->ofm_layout, param->pool_shape,
param->ofm_channels,
+ Array<IndexExpr>({1, 1}), param->strides, param->padding);
+ reporter->Assign(types[result_index], TensorType(ofm_shape, ifm->dtype));
+ return true;
+}
+
+Expr MakeEthosuPooling(Expr ifm, Expr lut, String pooling_type, double
ifm_scale,
+ int ifm_zero_point, double ofm_scale, int
ofm_zero_point,
+ Array<IndexExpr> pool_shape, IndexExpr ofm_channels,
+ Array<IndexExpr> strides, Array<IndexExpr> padding,
String activation,
+ int clip_min, int clip_max, String upscale, String
ifm_layout,
+ String ofm_layout) {
+ auto attrs = make_object<EthosuPoolingAttrs>();
+ attrs->pooling_type = pooling_type;
Review comment:
```suggestion
attrs->pooling_type = std::move(pooling_type);
```
##########
File path: tests/python/contrib/test_ethosu/test_type_inference.py
##########
@@ -92,5 +93,38 @@ def test_ethosu_depthwise_conv2d_type_inference(
assert tuple(f.body.checked_type.shape) == ofm_shape
[email protected](
+ "ifm_shape, ifm_layout", [((1, 56, 72, 55), "NHWC"), ((1, 56, 4, 72, 16),
"NHCWB16")]
+)
[email protected](
+ "ofm_shape, ofm_layout", [((1, 56, 38, 55), "NHWC"), ((1, 56, 4, 38, 16),
"NHCWB16")]
+)
+def test_ethosu_pooling_type_inference(
+ ifm_shape,
+ ifm_layout,
+ ofm_shape,
+ ofm_layout,
+):
+ ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
+ pooling_type = "AVG"
+ pool_shape = (3, 2)
+ ofm_channels = 55
+ strides = (1, 2)
+ padding = (0, 1, 2, 3)
+ pooling = make_ethosu_pooling(
+ ifm,
+ pooling_type,
+ pool_shape,
+ ofm_channels,
+ strides,
+ padding,
+ ifm_layout=ifm_layout,
+ ofm_layout=ofm_layout,
+ )
+ f = relay.Function([ifm], pooling)
+ f = run_opt_pass(f, relay.transform.InferType())
+ assert tuple(f.body.checked_type.shape) == ofm_shape
Review comment:
Could we test the output datatype is what's expected also?
##########
File path: python/tvm/relay/backend/contrib/ethosu/tir_to_cs_translator.py
##########
@@ -370,10 +372,9 @@ def translate_ethosu_conv2d(tir_call_extern: tvm.tir.Call)
-> Tuple[vapi.NpuConv
The vela object containing the params of ethosu_conv2d
weights_zero_point : int
The zero point of the weights
-
"""
# We skip the first element as it is the call_extern function name
- serial_object = spec.create_serial_object(spec.Serial2DConvolution,
tir_call_extern.args[1:])
+ serial_object = spec.create_serial_object(spec.Serial2DConvolution,
tir_extern_call.args[1:])
Review comment:
Just curious, why the change to `...extern_call` if this is a
`call_extern` in TIR?
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