areusch commented on code in PR #13752:
URL: https://github.com/apache/tvm/pull/13752#discussion_r1086036906
##########
python/tvm/relay/qnn/op/_qnn.py:
##########
@@ -85,12 +91,72 @@ def simulated_dequantize_compute(attrs, inputs,
output_type):
register_strategy("qnn.conv2d", strategy.qnn_conv2d_strategy)
+def _get_clip_dtype_bounds(dtype):
+ """Returns the minimum and maximum values of a C integer data type."""
+ assert "int" in dtype
+ bits = int(dtype[dtype.find("int") + 3 :])
+
+ if dtype.startswith("int"):
+ return (-(2 ** (bits - 1)), 2 ** (bits - 1) - 1)
+ elif dtype.startswith("uint"):
+ return (0, 2**bits - 1)
+ else:
+ raise TVMError(f"Clip legalization is not supported for data type
'{dtype}'!")
+
+
+@register_legalize("clip")
+def legalize_clip(attrs, inputs, tinfos):
Review Comment:
are there tests for these?
##########
python/tvm/topi/arm_cpu/qnn_alter_op.py:
##########
@@ -19,57 +19,108 @@
import numpy as np
from tvm import nd, relay, target
-from ..nn import qnn_requantize_alter_layout, qnn_add_alter_layout
+from ..utils import get_const_tuple
+from ..nn import qnn_conv2d_alter_layout, qnn_add_alter_layout,
qnn_requantize_alter_layout
-@qnn_requantize_alter_layout.register(["arm_cpu"])
-def alter_requantize_layout(attrs, inputs, _tinfos, _out_type):
- """Changes a floating point requantize op to use int64 multiply + shift
for microTVM.
+def prev_ops_match(curr_op, pattern):
Review Comment:
can you write a docstring here?
##########
src/target/source/codegen_c.cc:
##########
@@ -631,8 +632,10 @@ void CodeGenC::PrintVecBinaryOp(const std::string& op,
DataType t, PrimExpr lhs,
}
}
+NameSupply global_name_supply = NameSupply("");
void CodeGenC::VisitStmt_(const AllocateConstNode* op) {
- std::string symbol_name = op->buffer_var->name_hint;
+ std::string symbol_name =
global_name_supply->FreshName(op->buffer_var->name_hint);
Review Comment:
why was this needed? i think need to initialize the NameSupply from the
IRModule
##########
python/tvm/topi/arm_cpu/qnn_legalize.py:
##########
@@ -0,0 +1,349 @@
+# 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.
+"""QNN legalization transforms that help eliminate sparse channels.
+
+Some models (like MobileNetV1 when fine-tuned) have output channels in their
kernels which are
+completely full of zeros. Sometimes these can be optimized away by the C
compiler, but this does not
+happen when complex schedules (like the ACLE tensordot convolutions) are used.
+
+Instead, we will remove these channels by replacing blocks of operators with
equivalent "denser"
+ones during legalization. This is harder than it looks - while the outputs of
channels with all-zero
+kernels do not depend on the input data, they are usually not zero. We work
around this by computing
+how these constant values affect subsequent operators, and "folding" these
effects into a bias_add.
+
+It would eventually be nice to have a generalized, cross-target solution for
removing zero channels,
+as there is no downside. This may be possible with Relax, but I'm unsure.
+"""
+
+import numpy as np
+from scipy.signal import convolve2d
+from tvm.topi.utils import get_const_tuple
+from tvm import nd, relay
+from .qnn_alter_op import prev_ops_match, edit_attrs
+from ..nn import qnn_bias_add_legalize
+
+
+def _compute_fixed_conv2d_outputs(requantize_op):
+ """Compute all conv2d output values that do not depend on the layer
input."""
Review Comment:
could you document the return value, here and below?
##########
tests/python/relay/strategy/arm_cpu/test_quantized_convolution.py:
##########
@@ -95,6 +95,53 @@ def _get_mobilenet_v1_layer_attributes(layer_num):
return ((1, 1, 1, 1), (1, 1), True)
[email protected]("layer", range(2, 27, 2))
[email protected]_package("tensorflow")
+def test_empty_channel_detection(interpreter, layer):
+ """Some models (mainly MobileNetV1) have kernels with many output channels
full entirely of
+ zeroes. The VWW model is one of these. This test confirms that the outputs
of these channels,
+ as computed by TensorFlow, are indeed not dependent upon the input values.
+ """
+
+ _, kernel, bias, output = _load_tflite_layer(interpreter, layer)
+ kernel_data, _ = kernel
+ bias_data, bias_quant = bias
+ output_data, output_quant = output
+ is_depthwise = _get_mobilenet_v1_layer_attributes(layer)[2]
+ assert not is_depthwise
+ assert kernel_data.shape[1] == kernel_data.shape[2] == 1
+
+ out_channels = kernel_data.shape[3]
+ fixed_channels = {}
+
+ out_zero_point = output_quant["zero_points"][0]
+ assert out_zero_point == -128
+
+ for i in range(out_channels):
+ # Skip over output channels with data
+ if np.any(kernel_data[i, 0, 0, :]):
+ continue
+
+ scale = bias_quant["scales"][i] / output_quant["scales"][0]
+ channel_constant = round(bias_data[i] * scale + out_zero_point)
+ clipped = min(127, max(-128, channel_constant))
+
+ out_channel_values = output_data[0, :, :, i].flatten()
+ assert all(x == clipped for x in out_channel_values)
+ fixed_channels[i] = clipped
+
+ # We now need to compute values for the following depthwise layer
+ if layer == 26:
Review Comment:
anything else you can assert here other than `== 26`?
##########
python/tvm/topi/arm_cpu/qnn.py:
##########
@@ -368,3 +389,139 @@ def kernel_ptr(buffer, c, offset=0):
def schedule_qnn_depthwise_conv2d(_attrs, _outs, _target):
"""Schedule function for qnn.depthwise_conv2d."""
return None
+
+
+def _make_unrolled_conv2d_primfunc(
Review Comment:
can you document the parameters here?
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