You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/08/10 16:19:35 UTC
[GitHub] [tvm] jverma-quic commented on a diff in pull request #12340: [TOPI][Hexagon] Implement quantized avgpool
jverma-quic commented on code in PR #12340:
URL: https://github.com/apache/tvm/pull/12340#discussion_r942649464
##########
python/tvm/topi/hexagon/qnn/avg_pool2d.py:
##########
@@ -0,0 +1,180 @@
+# 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-variable, unused-argument, too-many-locals
+
+""" Compute and schedule for quantized avg_pool2d op
+
+Please note the following assumptions made by the implementation:
+
+1) The input must be padded in advance to account for 'padding'. In addition,
+ both input and output must be padded as per the physical buffer layout.
+2) The current implementation assumes 'count_include_pad' to be 'True'. It can be
+ modified to support 'False' case but the element count for the pooling window
+ must be pre-computed and provided as an input to reduce the run-time overhead.
+3) 'padding' is ignored. It must be handled outside of the sliced op.
+4) Please note that this implementation will not work if the output includes any
+ physical layout related padding as it can result into out-of-bound access
+ for the input.
+"""
+
+from tvm import te
+from tvm import tir
+from ..utils import get_layout_transform_fn, get_fixed_point_value
+
+
+def validate_out_shape(out_shape, in_shape, kernel, stride, dilation):
+ """Validate output shape"""
+ _, oh, ow, _ = out_shape
+ _, ih, iw, _ = in_shape
+ kh, kw = kernel
+ sh, sw = stride
+ dh, dw = dilation
+ if ih < (oh - 1) * sh + dh * (kh - 1) + 1:
+ raise RuntimeError("Output height is too large")
+ if iw < (ow - 1) * sw + dw * (kw - 1) + 1:
+ raise RuntimeError("Output width is too large")
+
+
+def saturate(x, dtype):
+ """Saturate value for the specified data type"""
+ if dtype == "uint8":
+ return te.max(0, te.min(x, 255))
+ elif dtype == "int8":
+ return te.max(-127, te.min(x, 128))
+ return x
+
+
+def qnn_avg_pool2d_compute(
+ data,
+ kernel,
+ stride,
+ dilation,
+ oshape,
+ odtype,
+ # quantization params:
+ input_zero_point,
+ input_scale,
+ output_zero_point,
+ output_scale,
+):
+ """Compute for quantized avg_pool2d"""
+ kh, kw = kernel
+ rh = te.reduce_axis((0, kh), name="rh")
+ rw = te.reduce_axis((0, kw), name="rw")
+ ob, oh, ow, oc = oshape
+ if isinstance(ob, int):
+ validate_out_shape(oshape, data.shape, kernel, stride, dilation)
+
+ if odtype == "uint8":
+ temp_dtype = "uint16"
+ elif odtype == "int8":
+ temp_dtype = "int16"
+ else:
+ raise RuntimeError(f"Unsupported output dtype, {odtype}'")
+
+ sh, sw = stride
+ dh, dw = dilation
+
+ PoolArea = kh * kw
+
+ scale = input_scale / output_scale
+ scale_fixed_point, rsh = get_fixed_point_value(scale, "int16")
+ scale_with_area = scale_fixed_point // PoolArea
+ corr = (output_zero_point << rsh) - input_zero_point * scale_fixed_point
+
+ Sum = te.compute(
+ oshape,
+ lambda b, h, w, c: te.sum(
+ data[b, h * sh + dh * rh, w * sw + dw * rw, c].astype(temp_dtype), axis=[rh, rw]
+ ),
+ name="sum",
+ )
+
+ Avg = te.compute(
+ oshape,
+ lambda b, h, w, c: saturate(
+ ((Sum[b, h, w, c] * scale_with_area) + corr) >> rsh, odtype
+ ).astype(odtype),
+ name="avg",
+ )
+ return Avg
+
+
+def schedule_nhwc_8h8w32c(outs, ins, output_layout: str, input_layout: str):
+ """Schedule for input and output layout nhwc-8h8w32c"""
+ func = te.create_prim_func([ins, outs])
+ s = tir.Schedule(func)
+ Sum = s.get_block("sum")
+ Avg = s.get_block("avg")
+
+ input_transform_fn = get_layout_transform_fn(input_layout)
+ output_transform_fn = get_layout_transform_fn(output_layout)
+ s.transform_layout(Sum, ("read", 0), input_transform_fn)
+ s.transform_layout(Avg, ("write", 0), output_transform_fn)
+
+ # Schedule 'Avg'
+ n, h, w, c = s.get_loops(Avg)
+ ho, hi = s.split(h, [None, 8])
+ wo, wi = s.split(w, [None, 8])
+ wio, wii = s.split(wi, [None, 4])
+ co, ci = s.split(c, [None, 32])
+ s.reorder(n, ho, wo, co, hi, wio, wii, ci)
+ wii_ci = s.fuse(wii, ci)
+ s.vectorize(wii_ci)
+
+ # Schedule 'Sum'
+ s.compute_at(Sum, wio)
+ Sum_axis = s.get_loops(Sum)
+ s.reorder(Sum_axis[-2], Sum_axis[-1], Sum_axis[-4], Sum_axis[-3])
+ ci_wii = s.fuse(Sum_axis[-4], Sum_axis[-3])
+ # s.vectorize(ci_wii) # Doesn't work
+ return s
+
+
+def schedule_n11c_2048c(outs, ins, output_layout: str, input_layout: str):
+ """Schedule for output layout: n11c-2048c, input layout: nhwc-8h8w32c"""
+ func = te.create_prim_func([ins, outs])
+ s = tir.Schedule(func)
+ Sum = s.get_block("sum")
+ Avg = s.get_block("avg")
+
+ input_transform_fn = get_layout_transform_fn(input_layout)
+ output_transform_fn = get_layout_transform_fn(output_layout)
+ s.transform_layout(Sum, ("read", 0), input_transform_fn)
+ s.transform_layout(Avg, ("write", 0), output_transform_fn)
+
+ # Schedule 'Avg'
+ n, h, w, c = s.get_loops(Avg)
+ co, ci = s.split(c, [None, 2048])
+ cio, cii = s.split(ci, [None, 128])
+ s.vectorize(cii)
+
+ # Schedule 'Sum'
+ s.compute_at(Sum, cio)
+ Sum_axis = s.get_loops(Sum)
+ s.reorder(Sum_axis[-2], Sum_axis[-1], Sum_axis[-3])
+ # s.vectorize(Sum_axis[-3]) # Doesn't work
+ return s
Review Comment:
Sure! I'll add some comments.
##########
python/tvm/topi/hexagon/utils.py:
##########
@@ -150,4 +156,91 @@ def get_layout_transform_fn(layout):
return nc_2048_2d
if layout == "nhwc-8h8w32c-2d":
return nhwc_8h8w32c_2d
+ if layout == "n11c-2048c-2d":
+ return n11c_2048c_2d
raise RuntimeError(f"Unexpected layout '{layout}'")
+
+
+def get_fixed_point_value(flp, dtype="int16"):
Review Comment:
I'll look into adding some test cases. Thanks!
##########
python/tvm/topi/hexagon/utils.py:
##########
@@ -150,4 +156,91 @@ def get_layout_transform_fn(layout):
return nc_2048_2d
if layout == "nhwc-8h8w32c-2d":
return nhwc_8h8w32c_2d
+ if layout == "n11c-2048c-2d":
+ return n11c_2048c_2d
raise RuntimeError(f"Unexpected layout '{layout}'")
+
+
+def get_fixed_point_value(flp, dtype="int16"):
Review Comment:
Good point! Thanks! The function handles denormalized values, but doesn't handle Nan or infinity. I'll add assert for these two cases. Will also include additional details on the denormalized case.
##########
python/tvm/topi/hexagon/qnn/avg_pool2d.py:
##########
@@ -0,0 +1,180 @@
+# 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-variable, unused-argument, too-many-locals
+
+""" Compute and schedule for quantized avg_pool2d op
+
+Please note the following assumptions made by the implementation:
+
+1) The input must be padded in advance to account for 'padding'. In addition,
+ both input and output must be padded as per the physical buffer layout.
+2) The current implementation assumes 'count_include_pad' to be 'True'. It can be
+ modified to support 'False' case but the element count for the pooling window
+ must be pre-computed and provided as an input to reduce the run-time overhead.
+3) 'padding' is ignored. It must be handled outside of the sliced op.
+4) Please note that this implementation will not work if the output includes any
+ physical layout related padding as it can result into out-of-bound access
+ for the input.
+"""
+
+from tvm import te
+from tvm import tir
+from ..utils import get_layout_transform_fn, get_fixed_point_value
+
+
+def validate_out_shape(out_shape, in_shape, kernel, stride, dilation):
+ """Validate output shape"""
+ _, oh, ow, _ = out_shape
+ _, ih, iw, _ = in_shape
+ kh, kw = kernel
+ sh, sw = stride
+ dh, dw = dilation
+ if ih < (oh - 1) * sh + dh * (kh - 1) + 1:
+ raise RuntimeError("Output height is too large")
+ if iw < (ow - 1) * sw + dw * (kw - 1) + 1:
+ raise RuntimeError("Output width is too large")
+
+
+def saturate(x, dtype):
+ """Saturate value for the specified data type"""
+ if dtype == "uint8":
+ return te.max(0, te.min(x, 255))
+ elif dtype == "int8":
+ return te.max(-127, te.min(x, 128))
+ return x
+
+
+def qnn_avg_pool2d_compute(
+ data,
+ kernel,
+ stride,
+ dilation,
+ oshape,
+ odtype,
+ # quantization params:
+ input_zero_point,
+ input_scale,
+ output_zero_point,
+ output_scale,
+):
+ """Compute for quantized avg_pool2d"""
+ kh, kw = kernel
+ rh = te.reduce_axis((0, kh), name="rh")
+ rw = te.reduce_axis((0, kw), name="rw")
+ ob, oh, ow, oc = oshape
+ if isinstance(ob, int):
+ validate_out_shape(oshape, data.shape, kernel, stride, dilation)
+
+ if odtype == "uint8":
+ temp_dtype = "uint16"
+ elif odtype == "int8":
+ temp_dtype = "int16"
+ else:
+ raise RuntimeError(f"Unsupported output dtype, {odtype}'")
+
+ sh, sw = stride
+ dh, dw = dilation
+
+ PoolArea = kh * kw
+
+ scale = input_scale / output_scale
+ scale_fixed_point, rsh = get_fixed_point_value(scale, "int16")
Review Comment:
rsh (short for right shift) and it's log2(scale_factor). Sorry, I couldn't really think of a better names (may be log2_scale_factor). If you've any suggestions, please do share.
##########
python/tvm/topi/hexagon/qnn/avg_pool2d.py:
##########
@@ -0,0 +1,180 @@
+# 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-variable, unused-argument, too-many-locals
+
+""" Compute and schedule for quantized avg_pool2d op
+
+Please note the following assumptions made by the implementation:
+
+1) The input must be padded in advance to account for 'padding'. In addition,
+ both input and output must be padded as per the physical buffer layout.
+2) The current implementation assumes 'count_include_pad' to be 'True'. It can be
+ modified to support 'False' case but the element count for the pooling window
+ must be pre-computed and provided as an input to reduce the run-time overhead.
+3) 'padding' is ignored. It must be handled outside of the sliced op.
+4) Please note that this implementation will not work if the output includes any
+ physical layout related padding as it can result into out-of-bound access
+ for the input.
+"""
+
+from tvm import te
+from tvm import tir
+from ..utils import get_layout_transform_fn, get_fixed_point_value
+
+
+def validate_out_shape(out_shape, in_shape, kernel, stride, dilation):
+ """Validate output shape"""
+ _, oh, ow, _ = out_shape
+ _, ih, iw, _ = in_shape
+ kh, kw = kernel
+ sh, sw = stride
+ dh, dw = dilation
+ if ih < (oh - 1) * sh + dh * (kh - 1) + 1:
+ raise RuntimeError("Output height is too large")
+ if iw < (ow - 1) * sw + dw * (kw - 1) + 1:
+ raise RuntimeError("Output width is too large")
+
+
+def saturate(x, dtype):
+ """Saturate value for the specified data type"""
+ if dtype == "uint8":
+ return te.max(0, te.min(x, 255))
+ elif dtype == "int8":
+ return te.max(-127, te.min(x, 128))
+ return x
+
+
+def qnn_avg_pool2d_compute(
+ data,
Review Comment:
Yes, there are some assumptions made which are listed at the top of the file.
##########
python/tvm/topi/hexagon/qnn/avg_pool2d.py:
##########
@@ -0,0 +1,180 @@
+# 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-variable, unused-argument, too-many-locals
+
+""" Compute and schedule for quantized avg_pool2d op
+
+Please note the following assumptions made by the implementation:
+
+1) The input must be padded in advance to account for 'padding'. In addition,
+ both input and output must be padded as per the physical buffer layout.
+2) The current implementation assumes 'count_include_pad' to be 'True'. It can be
+ modified to support 'False' case but the element count for the pooling window
+ must be pre-computed and provided as an input to reduce the run-time overhead.
+3) 'padding' is ignored. It must be handled outside of the sliced op.
+4) Please note that this implementation will not work if the output includes any
+ physical layout related padding as it can result into out-of-bound access
+ for the input.
+"""
+
+from tvm import te
+from tvm import tir
+from ..utils import get_layout_transform_fn, get_fixed_point_value
+
+
+def validate_out_shape(out_shape, in_shape, kernel, stride, dilation):
+ """Validate output shape"""
+ _, oh, ow, _ = out_shape
+ _, ih, iw, _ = in_shape
+ kh, kw = kernel
+ sh, sw = stride
+ dh, dw = dilation
+ if ih < (oh - 1) * sh + dh * (kh - 1) + 1:
+ raise RuntimeError("Output height is too large")
+ if iw < (ow - 1) * sw + dw * (kw - 1) + 1:
+ raise RuntimeError("Output width is too large")
+
+
+def saturate(x, dtype):
+ """Saturate value for the specified data type"""
+ if dtype == "uint8":
+ return te.max(0, te.min(x, 255))
+ elif dtype == "int8":
+ return te.max(-127, te.min(x, 128))
Review Comment:
Sure, will look into it.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: commits-unsubscribe@tvm.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org