areusch commented on code in PR #11250:
URL: https://github.com/apache/tvm/pull/11250#discussion_r871662035
##########
python/tvm/topi/arm_cpu/conv2d_int8.py:
##########
@@ -126,7 +126,7 @@ def is_int8_hw_support(data_dtype, kernel_dtype):
# 3) Check target
is_target_support = is_neon_available() or is_dotprod_available()
- return is_dtype_support and is_llvm_support
+ return is_dtype_support and is_llvm_support and is_target_support
Review Comment:
is this one a carryover from #11193 ?
##########
python/tvm/autotvm/task/dispatcher.py:
##########
@@ -178,6 +178,50 @@ def update(self, target, workload, cfg):
self._config = cfg
+class ApplyFixedConfig(DispatchContext):
+ """Apply a config of a deterministic schedule.
+
+ Parameters
+ ----------
+ tasks : list[tvm.autotvm.task.task.Task]
+ List of autoTVM tasks.
+ schedule_name : str
+ Name of schedule to use.
+ """
+
+ def __init__(self, tasks, schedule_name: str):
Review Comment:
for schedule_name, should we be taking a mapping here, e.g. a dict mapping
compute function name to schedule or something?
##########
python/tvm/micro/testing/aot_test_utils.py:
##########
@@ -38,12 +36,13 @@
import tvm
from tvm import relay
from tvm import te
+from tvm import autotvm
Review Comment:
i'm wondering if we shold place this file in python/tvm/testing instead? AOT
is not specific to micro. we could also split the Corstone stuff into micro.
##########
python/tvm/autotvm/task/dispatcher.py:
##########
@@ -178,6 +178,50 @@ def update(self, target, workload, cfg):
self._config = cfg
+class ApplyFixedConfig(DispatchContext):
+ """Apply a config of a deterministic schedule.
Review Comment:
could you explain why this is different from ApplyConfig here?
##########
python/tvm/autotvm/task/dispatcher.py:
##########
@@ -178,6 +178,50 @@ def update(self, target, workload, cfg):
self._config = cfg
+class ApplyFixedConfig(DispatchContext):
+ """Apply a config of a deterministic schedule.
+
+ Parameters
+ ----------
+ tasks : list[tvm.autotvm.task.task.Task]
+ List of autoTVM tasks.
+ schedule_name : str
+ Name of schedule to use.
+ """
+
+ def __init__(self, tasks, schedule_name: str):
+ super(ApplyFixedConfig, self).__init__()
+ self._schedule_name = schedule_name
+ self._tasks = tasks
+ self.workload = None
+
+ def _query_inside(self, target, workload):
+ """Override query"""
+ self.workload = workload
+
+ # Creat a config from correct task
+ for task in self._tasks:
+ if task.name == workload[0]:
+ config = task.config_space.get(0)
+ break
+
+ if not config:
+ raise RuntimeError(
+ "workload: %s does not exist in %s" % (str(workload),
str(self._tasks))
+ )
+ # Add low cost to the target schedule and high cost to others.
+ if workload[0] == self._schedule_name:
+ config.cost = 0.000001
Review Comment:
better is 1e-6
##########
tests/python/relay/strategy/arm_cpu/test_conv2d_nhwc.py:
##########
@@ -0,0 +1,163 @@
+# 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 sys
+import numpy as np
+import pytest
+import tvm
+import tvm.testing
+from tvm import relay
+from tvm.micro.testing.aot_test_utils import (
+ AOTTestModel,
+ AOT_CORSTONE300_RUNNER,
+ generate_ref_data,
+ compile_and_run,
+)
+
+
+class BasicConv2dTests:
+ @tvm.testing.requires_corstone300
+ def test_conv2d(
+ self,
+ data_shape,
+ kernel_size,
+ kernel_layout,
+ num_filter,
+ strides,
+ padding,
+ dilation,
+ dtype,
+ schedule_name,
+ ):
+ """Test a subgraph with a single conv2d operator."""
+ ishape = data_shape
+ wshape = (*kernel_size, data_shape[-1], num_filter)
+
+ weight_data = np.random.randint(low=-10, high=10, size=wshape,
dtype=dtype)
+
+ input0 = relay.var("input", relay.TensorType(ishape, dtype))
+ weight0 = relay.const(weight_data)
+ out0 = relay.op.nn.conv2d(
+ input0,
+ weight0,
+ kernel_size=kernel_size,
+ strides=strides,
+ padding=padding,
+ dilation=(dilation, dilation),
+ data_layout="NHWC",
+ kernel_layout="HWIO",
+ out_dtype="int32",
+ out_layout="NHWC",
+ )
+ ref_mod = tvm.IRModule.from_expr(relay.Function([input0], out0))
+
+ input1 = relay.var("input", relay.TensorType(ishape, dtype))
+
+ if kernel_layout == "HWOI":
+ weight1 = relay.const(np.moveaxis(weight_data, 2, -1))
+ elif kernel_layout == "HWIO":
+ weight1 = relay.const(weight_data)
+
+ out1 = relay.op.nn.conv2d(
+ input1,
+ weight1,
+ kernel_size=kernel_size,
+ strides=strides,
+ padding=padding,
+ dilation=(dilation, dilation),
+ data_layout="NHWC",
+ kernel_layout=kernel_layout,
+ out_dtype="int32",
+ out_layout="NHWC",
+ )
+ mod = tvm.IRModule.from_expr(relay.Function([input1], out1))
+
+ inputs = {"input": np.random.randint(low=-128, high=127, size=ishape,
dtype=dtype)}
+ output_list = generate_ref_data(ref_mod, inputs)
+
+ compile_and_run(
+ AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
+ runner=AOT_CORSTONE300_RUNNER,
+ interface_api="c",
+ use_unpacked_api=True,
+ target_opts={
+ "-keys": "arm_cpu",
+ "-mcpu": "cortex-m7",
+ },
+ schedule_name=schedule_name,
+ )
+
+
+class TestConv2d_DSP_HWOI(BasicConv2dTests):
+ """This test is for conv2d_nhwc_dsp.arm_cpu schedule."""
+
+ data_shape, kernel_size, num_filter, strides, padding, dilation =
tvm.testing.parameters(
+ # TODO(mehrdadh): Fails due to
https://github.com/apache/tvm/issues/11216
+ # ((1, 32, 32, 1), (3, 3), 12, 1, 0, 1),
+ # ((1, 32, 10, 3), (3, 3), 16, 1, 0, 1),
+ # ((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 2),
+ ((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2),
+ # from Keyword Spotting model from MLPerfTiny models
+ # TODO(mehrdad): Fails due to
https://github.com/apache/tvm/issues/11216
+ # ((1, 49, 10, 1), (10, 4), 64, (2, 2), (4, 1, 5, 1), 1),
+ # from Visual Wake Word model from MLPerfTiny models
+ # TODO(mehrdadh): fails due to
https://github.com/apache/tvm/issues/11216
+ # ((1, 96, 96, 3), (3, 3), 8, (2, 2), (0, 0, 1, 1), 1),
+ # from Image Classification model from MLPerfTiny models
+ ((1, 16, 16, 32), (1, 1), 64, (2, 2), 0, 1),
+ ((4, 16, 16, 8), (5, 5), 8, 2, (0, 4, 4, 0), 1),
+ ((4, 16, 16, 8), (5, 5), 16, 2, (0, 4, 4, 0), 1),
+ ((4, 16, 16, 8), (5, 5), 8, 2, 0, 1),
+ ((4, 16, 16, 8), (5, 5), 16, 2, 0, 1),
+ ((1, 16, 16, 8), (3, 3), 16, 2, (0, 0, 1, 1), 1),
+ ((1, 16, 16, 8), (3, 3), 16, 2, (1, 1, 2, 2), 1),
+ ((1, 16, 16, 8), (5, 5), 16, 2, (3, 3, 2, 2), 1),
+ ((1, 16, 16, 8), (3, 3), 16, 2, (0, 1, 2, 3), 1),
+ )
+ dtype = tvm.testing.parameter("int8", "int16")
+ kernel_layout = tvm.testing.parameter("HWOI")
+ schedule_name = tvm.testing.parameter("conv2d_nhwc_dsp.arm_cpu")
+
+
+class TestConv2d_HWIO(BasicConv2dTests):
+ """This test is for conv2d_nhwc_spatial_pack.arm_cpu schedule."""
+
+ data_shape, kernel_size, num_filter, strides, padding, dilation =
tvm.testing.parameters(
+ ((1, 32, 32, 1), (3, 3), 12, 1, 0, 1),
+ ((1, 32, 10, 3), (3, 3), 16, 1, 0, 1),
+ ((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1),
+ ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 2),
+ ((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2),
+ )
+ dtype = tvm.testing.parameter("int8", "int16")
+ kernel_layout = tvm.testing.parameter("HWIO")
+ schedule_name = tvm.testing.parameter("conv2d_nhwc_spatial_pack.arm_cpu")
+
+
+# TODO(mehrdadh): Add test for conv2d_NHWC_quantized_native.arm_cpu
Review Comment:
prefer to reference a GH issue rather than a person if possible
##########
python/tvm/micro/testing/aot_test_utils.py:
##########
@@ -708,31 +708,52 @@ def compile_models(
compiled_mods = list()
for model in models:
- with tvm.transform.PassContext(opt_level=3, config=config):
- # TODO(Mousius) - Remove once executor/runtime are fully removed
from Target
- if use_runtime_executor:
- executor_factory = tvm.relay.build(
- model.module,
- target,
- executor=executor,
- runtime=runtime,
- workspace_memory_pools=workspace_memory_pools,
- params=model.params,
- mod_name=model.name,
- )
- compiled_mods.append(
- AOTCompiledTestModel(model=model,
executor_factory=executor_factory)
- )
- else:
- executor_factory = tvm.relay.build(
- model.module,
- tvm.target.Target(target, host=target),
- params=model.params,
- mod_name=model.name,
- )
- compiled_mods.append(
- AOTCompiledTestModel(model=model,
executor_factory=executor_factory)
- )
+ if schedule_name:
Review Comment:
could we add some kind of assert here to verify that tvm.relay.build
actually chose this schedule?
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