NicolaLancellotti commented on a change in pull request #9384:
URL: https://github.com/apache/tvm/pull/9384#discussion_r740305018
##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -458,7 +458,102 @@ def verify(ext_func):
mod = partition_ethosu_by_table(mod, depthwise_pattern_table)
mod["tvmgen_default_ethosu_main_0"] = dataflow_pattern.rewrite(
- legalize.EthosuDepthwiseConv2DRewriter(),
mod["tvmgen_default_ethosu_main_0"]
+ legalize.DepthwiseConv2DRewriter(), mod["tvmgen_default_ethosu_main_0"]
+ )
+ verify(mod["tvmgen_default_ethosu_main_0"])
+
+
[email protected]("pooling_type", ["MAX", "AVG"])
[email protected]("ifm_shape", [[1, 9, 12, 3], [1, 10, 20, 2]])
[email protected]("strides", [[1, 2], [2, 3]])
[email protected]("pool_shape", [[1, 2], [2, 3]])
[email protected]("activation_function", ["NONE", "RELU"])
[email protected]("padding", ["SAME", "VALID"])
+def test_tflite_pool2d_legalize(
+ ifm_shape, pooling_type, strides, pool_shape, activation_function, padding
+):
+ dtype = "int8"
+
+ def create_tflite_graph():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ if pooling_type == "MAX":
+ op = tf.nn.max_pool(x, pool_shape, strides, padding)
+ elif pooling_type == "AVG":
+ op = tf.nn.avg_pool(x, pool_shape, strides, padding)
+ if activation_function == "RELU":
+ op = tf.nn.relu(op)
+ return op
+
+ model = Model()
+ concrete_func = model.tf_function.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32)
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ data = np.random.rand(*tuple(ifm_shape))
+ yield [data.astype(np.float32)]
+
+ converter =
tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops =
[tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ def verify(ext_func):
+ ofm_shape = infra.compute_ofm_shape(ifm_shape, padding, pool_shape,
strides)
+ op = ext_func.body
+ assert list(op.args[0].checked_type.shape) == ifm_shape
+ assert op.args[0].checked_type.dtype == dtype
+ assert list(op.checked_type.shape) == ofm_shape
+ assert op.checked_type.dtype == dtype
+ assert op.attrs.pooling_type == pooling_type
+ assert list(op.attrs.strides) == strides
+ assert list(op.attrs.padding) == infra.compute_padding_shape(
+ ifm_shape, ofm_shape, padding, pool_shape, strides
+ )
+ assert list(op.attrs.pool_shape) == pool_shape
+ assert op.attrs.ofm_channels == ifm_shape[3]
+ if activation_function == "RELU":
+ assert str(op.attrs.activation) == "CLIP"
+
+ if pooling_type == "MAX":
+ rewriter = legalize.MaxPoolingRewriter()
+ pattern_table = [
+ (
+ ethosu.MaxPool2DParams.composite_name,
+ ethosu.qnn_maxpool2d_pattern(),
+ lambda pat: ethosu.MaxPool2DParams(pat).is_valid(),
+ ),
+ ]
+ elif pooling_type == "AVG":
+ rewriter = legalize.AvgPoolingRewriter()
+ pattern_table = [
+ (
+ ethosu.AvgPool2DParams.composite_name,
+ ethosu.qnn_avgpool2d_pattern(),
+ lambda pat: ethosu.AvgPool2DParams(pat).is_valid(),
+ ),
+ ]
Review comment:
Thinking about it, it should be ok to have only one test, that because
`MAX` and `AVG` are the two possible values of the `pooling_type` attribute of
the Ethos(TM)-U Pooling operator. We are not testing two different operators in
one test, but different parameters of the same operator.
##########
File path: src/relay/op/contrib/ethosu/pooling.cc
##########
@@ -0,0 +1,185 @@
+/*
+ * 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 the Output Feature Map 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.";
+
+ ICHECK(param->pooling_type == "AVG" || param->pooling_type == "MAX")
Review comment:
Done
##########
File path: src/relay/op/contrib/ethosu/pooling.cc
##########
@@ -0,0 +1,185 @@
+/*
+ * 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 the Output Feature Map 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.";
+
+ ICHECK(param->pooling_type == "AVG" || 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:
Done
##########
File path: tests/python/contrib/test_ethosu/test_replace_pooling.py
##########
@@ -0,0 +1,158 @@
+# 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
+
+pytest.importorskip("ethosu.vela")
+
+import tvm
+from tvm import relay
+from tvm.relay.testing import run_opt_pass
+from tvm.relay.backend.contrib.ethosu.tir import spec
+from tvm.relay.backend.contrib.ethosu.tir.compiler import lower_to_tir
+from .infra import make_ethosu_pooling, get_pooling_args
+
+
[email protected](
+ "ifm_shape, ofm_channels, ifm_layout, ofm_layout",
+ [
+ ((1, 5, 9, 3), 3, "NHWC", "NHWC"),
+ ((1, 8, 3, 9, 16), 40, "NHCWB16", "NHCWB16"),
+ ((1, 8, 3, 9, 16), 40, "NHCWB16", "NHWC"),
+ ((1, 8, 9, 40), 40, "NHWC", "NHCWB16"),
+ ],
+)
[email protected]("pooling_type", ["AVG", "MAX"])
[email protected]("activation", ["NONE", "CLIP", "TANH", "SIGMOID"])
+def test_pooling_single(
+ ifm_shape,
+ ofm_channels,
+ ifm_layout,
+ ofm_layout,
+ pooling_type,
+ activation,
+):
+ pool_shape = (3, 2)
+ strides = (1, 2)
+ padding = (1, 1, 1, 0)
+ ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
+ pooling = make_ethosu_pooling(
+ ifm,
+ pooling_type,
+ pool_shape,
+ ofm_channels,
+ strides,
+ padding,
+ activation,
+ ifm_layout,
+ ofm_layout,
+ )
+ func = relay.Function(relay.analysis.free_vars(pooling), pooling)
+ func = run_opt_pass(func, relay.transform.InferType())
+ mod, _ = lower_to_tir(func)
+ data = []
+
+ def _visit(stmt):
+ if isinstance(stmt, tvm.tir.Call):
+ data.append(get_pooling_args(stmt))
+
+ tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
+ if ifm_layout == "NHWC":
+ ifm_stride_c = 1
+ ifm_stride_w = ifm_shape[3]
+ ifm_stride_h = ifm_shape[2] * ifm_shape[3]
+ ofm_height = (ifm_shape[1] - pool_shape[0] + padding[0] + padding[0])
// strides[0] + 1
+ ofm_width = (ifm_shape[2] - pool_shape[1] + padding[1] + padding[1])
// strides[1] + 1
+ else:
+ ifm_stride_w = 16
+ ifm_stride_c = 16 * ifm_shape[3]
+ ifm_stride_h = 16 * ifm_shape[2] * ifm_shape[3]
+ ofm_height = (ifm_shape[1] - pool_shape[0] + padding[0] + padding[0])
// strides[0] + 1
+ ofm_width = (ifm_shape[3] - pool_shape[1] + padding[1] + padding[1])
// strides[1] + 1
+
+ if ofm_layout == "NHWC":
+ ofm_stride_c = 1
+ ofm_stride_w = ofm_channels if ofm_width > 1 else 1
+ ofm_stride_h = ofm_channels * ofm_width if ofm_height > 1 else 1
+ else:
+ ofm_stride_w = 16
+ ofm_stride_c = 16 * ofm_width
+ ofm_stride_h = 16 * ofm_width * ((ofm_channels - 1) // 16 + 1)
+
+ serial_pooling = spec.SerialPooling(
+ ifm=spec.SerialFeatureMap(
+ data_type="int8",
+ height=ifm_shape[1],
+ width=ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
+ channels=ofm_channels,
+ tile_height_0=ifm_shape[1],
+ tile_height_1=0,
+ tile_width_0=ifm_shape[2] if ifm_layout == "NHWC" else
ifm_shape[3],
+ tile_address_0=0,
+ tile_address_1=0,
+ tile_address_2=0,
+ tile_address_3=0,
+ scale=1.0,
+ zero_point=0,
+ layout=ifm_layout,
+ stride_h=ifm_stride_h,
+ stride_w=ifm_stride_w,
+ stride_c=ifm_stride_c,
+ ),
+ ofm=spec.SerialFeatureMap(
+ data_type="int8",
+ height=ofm_height,
+ width=ofm_width,
+ channels=ofm_channels,
+ tile_height_0=ofm_height,
+ tile_height_1=0,
+ tile_width_0=ofm_width,
+ tile_address_0=0,
+ tile_address_1=0,
+ tile_address_2=0,
+ tile_address_3=0,
+ scale=1.0,
+ zero_point=0,
+ layout=ofm_layout,
+ stride_h=ofm_stride_h,
+ stride_w=ofm_stride_w,
+ stride_c=ofm_stride_c,
+ ),
+ pooling_type=pooling_type,
+ pool_shape=spec.SerialKernel(
+ width=pool_shape[1],
+ height=pool_shape[0],
+ stride_w=strides[1],
+ stride_h=strides[0],
+ dilation_w=1,
+ dilation_h=1,
+ ),
+ padding=spec.SerialPadding(
+ top=padding[0], left=padding[1], bottom=padding[2],
right=padding[3]
+ ),
+ activation=spec.SerialActivation(
+ op=activation,
+ clip_min=10 if activation == "CLIP" else 0,
+ clip_max=100 if activation == "CLIP" else 0,
+ ),
Review comment:
I re-added the `NONE` test
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