NicolaLancellotti commented on a change in pull request #9384:
URL: https://github.com/apache/tvm/pull/9384#discussion_r738596287
##########
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]
+ self.strides = attrs.strides
+ self.padding = attrs.padding
+ self.activation = clip
+ self.pooling_type = "MAX"
+
+ def is_valid(self):
+ """
+ This function checks whether MaxPool2D has compatible attributes with
the NPU
+ """
+ tensor_params = [self.ifm, self.ofm]
+ if not check_valid_dtypes(tensor_params):
+ return False
+ if self.ifm.dtype != self.ofm.dtype:
+ return False
+ if not check_strides(self.strides):
+ return False
+ if not check_batch_size(self.ifm):
+ return False
+ if not check_padding(self.padding, self.padding_bounds):
+ return False
+ # Check pool size
+ if (
+ len(self.pool_shape) != 2
+ or self.pool_shape[1] > 256
+ or self.pool_shape[0] * self.pool_shape[1] > 256 * 256
+ ):
+ return False
+ return True
Review comment:
I'd rather the current approach, that is, a list of `if` guards that
checks if it is not valid, and at the end, it returns True otherwise. What do
other people think?
##########
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]
+ self.strides = attrs.strides
+ self.padding = attrs.padding
+ self.activation = clip
+ self.pooling_type = "MAX"
+
+ def is_valid(self):
+ """
+ This function checks whether MaxPool2D has compatible attributes with
the NPU
+ """
+ tensor_params = [self.ifm, self.ofm]
+ if not check_valid_dtypes(tensor_params):
+ return False
+ if self.ifm.dtype != self.ofm.dtype:
+ return False
+ if not check_strides(self.strides):
+ return False
+ if not check_batch_size(self.ifm):
+ return False
+ if not check_padding(self.padding, self.padding_bounds):
+ return False
+ # Check pool size
+ if (
+ len(self.pool_shape) != 2
+ or self.pool_shape[1] > 256
+ or self.pool_shape[0] * self.pool_shape[1] > 256 * 256
+ ):
+ return False
+ return True
+
+
+def qnn_maxpool2d_pattern() -> tvm.relay.dataflow_pattern.DFPattern:
+ """
+ This function creates the pattern for nn.max_pool2d with optional fused
RELU activation.
+ """
+ pattern = is_op("nn.max_pool2d")(wildcard())
+ pattern = pattern.optional(is_op("clip"))
+ return pattern
+
+
+class AvgPool2DParams:
+ """
+ This class will parse a call to a ethosu.avgpool2d composite function
+ and extract the parameter information.
+ """
+
+ composite_name = "ethosu.avgpool2d"
+ # 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
+
+ cast2 = pool_op
+ avgpool = cast2.args[0]
+ cast1 = avgpool.args[0]
+ layout = avgpool.attrs.layout
+ self.ifm = TensorParams(cast1.args[0], layout)
+ self.ofm = TensorParams(cast2, layout)
+ self.pool_shape = [int(i) for i in avgpool.attrs.pool_size]
+ self.strides = avgpool.attrs.strides
+ self.padding = avgpool.attrs.padding
+ self.activation = clip
+ self.pooling_type = "AVG"
+
+ def is_valid(self):
+ """
+ This function checks whether AvgPool2D has compatible attributes with
the NPU
+ """
+ tensor_params = [self.ifm, self.ofm]
+ if not check_valid_dtypes(tensor_params):
+ return False
+ if self.ifm.dtype != self.ofm.dtype:
+ return False
+ if not check_strides(self.strides):
+ return False
+ if not check_batch_size(self.ifm):
+ return False
+ if not check_padding(self.padding, self.padding_bounds):
+ return False
+ # Check pool size
+ if (
+ len(self.pool_shape) != 2
+ or self.pool_shape[1] > 256
+ or self.pool_shape[0] * self.pool_shape[1] > 256 * 256
+ ):
+ return False
+ return True
Review comment:
The same 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.")
+ .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))
Review comment:
The checks should always be true, because we check the values during the
legalization.
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