[GitHub] [incubator-tvm] masahi commented on issue #4799: [QNN] Doc fix on convolution and dequantize
masahi commented on issue #4799: [QNN] Doc fix on convolution and dequantize
URL: https://github.com/apache/incubator-tvm/pull/4799#issuecomment-580980672
this is the error I get if I leave out kernel_size:
```
File "/home/masa/projects/dev/tvm/python/tvm/_ffi/_ctypes/function.py",
line 72, in cfun
rv = local_pyfunc(*pyargs)
File "/home/masa/projects/dev/tvm/python/tvm/relay/op/nn/_nn.py", line
269, in alter_op_layout_conv2d
return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, op)
File
"",
line 2, in conv2d_alter_layout
File "/home/masa/projects/dev/tvm/python/tvm/target.py", line 382, in
dispatch_func
return dispatch_dict[k](*args, **kwargs)
File
"/home/masa/projects/dev/tvm/topi/python/topi/x86/conv2d_alter_op.py", line 45,
in _alter_conv2d_layout
kh, kw = attrs.get_int_tuple("kernel_size")
File "/home/masa/projects/dev/tvm/python/tvm/attrs.py", line 63, in
get_int_tuple
return tuple(x.value for x in self.__getattr__(key))
TypeError: 'NoneType' object is not iterable
```
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[GitHub] [incubator-tvm] masahi opened a new pull request #4799: [QNN] Doc fix on convolution and dequantize
masahi opened a new pull request #4799: [QNN] Doc fix on convolution and dequantize URL: https://github.com/apache/incubator-tvm/pull/4799 cc @anijain2305 @vinx13 can you check if this change is correct? I'm not completely sure if kernel_size is required, but I hit error if I leave it none. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 merged pull request #4775: conv3d_ndhwc schedule
icemelon9 merged pull request #4775: conv3d_ndhwc schedule URL: https://github.com/apache/incubator-tvm/pull/4775 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on issue #4775: conv3d_ndhwc schedule
icemelon9 commented on issue #4775: conv3d_ndhwc schedule URL: https://github.com/apache/incubator-tvm/pull/4775#issuecomment-580976944 Thanks @alexgl-github . This is now merged. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (90b2a1e -> cf173fd)
This is an automated email from the ASF dual-hosted git repository. haichen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 90b2a1e [Relay][Topi] Use SimplifyInference for L2 Normazlization. (#4795) add cf173fd Add schedule for conv3d NDHWC layout (#4775) No new revisions were added by this update. Summary of changes: topi/python/topi/nn/conv3d.py| 14 +++ topi/python/topi/x86/__init__.py | 1 + topi/python/topi/x86/conv3d.py | 82 3 files changed, 90 insertions(+), 7 deletions(-) create mode 100644 topi/python/topi/x86/conv3d.py
[GitHub] [incubator-tvm] anijain2305 opened a new pull request #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply.
anijain2305 opened a new pull request #4798: [QNN] Optimize lowering for requantize and FixedPointMultiply. URL: https://github.com/apache/incubator-tvm/pull/4798 As Title. Changes are verified through existing tests. @jackwish @FrozenGene @yzhliu @vinx13 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 opened a new pull request #4797: [AutoTVM] Minor bug fixes in AutoTVM for QNN graphs
anijain2305 opened a new pull request #4797: [AutoTVM] Minor bug fixes in AutoTVM for QNN graphs URL: https://github.com/apache/incubator-tvm/pull/4797 I encountered many bugs during autotuning, both kernel and graph, for a QNN graph. This PR adds fixes all the minor bugs. There is one major implementation remaining for fixing the graph tuner. More on that in a separate PR. @icemelon9 @yzhliu @kevinthesun 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] wweic commented on issue #4628: [Object] Add String container
wweic commented on issue #4628: [Object] Add String container URL: https://github.com/apache/incubator-tvm/pull/4628#issuecomment-580966470 @tqchen I'll send new revision soon. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4644: [WIP] Relay op strategy
tqchen commented on issue #4644: [WIP] Relay op strategy URL: https://github.com/apache/incubator-tvm/pull/4644#issuecomment-580955527 cc @merrymercy @vinx13 @ZihengJiang @jwfromm please help review if you have time 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #4775: conv3d_ndhwc schedule
icemelon9 commented on a change in pull request #4775: conv3d_ndhwc schedule
URL: https://github.com/apache/incubator-tvm/pull/4775#discussion_r373715242
##
File path: topi/python/topi/x86/conv3d.py
##
@@ -0,0 +1,85 @@
+# 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, too-many-locals
+# pylint: disable=unused-argument, redefined-builtin, no-else-return
+"""Conv3D operators"""
+import tvm
+from .. import generic, tag
+from ..util import traverse_inline
+
+@generic.schedule_conv3d_ndhwc.register("cpu")
+def schedule_conv3d_ndhwc(outs):
+"""TOPI schedule callback for conv3d
+
+Parameters
+--
+outs: Array of Tensor
+The computation graph description of conv3d
+in the format of an array of tensors.
+
+Returns
+---
+s: Schedule
+The computation schedule for conv3d.
+"""
+s = tvm.create_schedule([x.op for x in outs])
+output_op = outs[0].op
+scheduled_ops = []
Review comment:
Remove this line since it's useless now
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[GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #4775: conv3d_ndhwc schedule
icemelon9 commented on a change in pull request #4775: conv3d_ndhwc schedule
URL: https://github.com/apache/incubator-tvm/pull/4775#discussion_r373715263
##
File path: topi/python/topi/x86/conv3d.py
##
@@ -0,0 +1,85 @@
+# 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, too-many-locals
+# pylint: disable=unused-argument, redefined-builtin, no-else-return
+"""Conv3D operators"""
+import tvm
+from .. import generic, tag
+from ..util import traverse_inline
+
+@generic.schedule_conv3d_ndhwc.register("cpu")
+def schedule_conv3d_ndhwc(outs):
+"""TOPI schedule callback for conv3d
+
+Parameters
+--
+outs: Array of Tensor
+The computation graph description of conv3d
+in the format of an array of tensors.
+
+Returns
+---
+s: Schedule
+The computation schedule for conv3d.
+"""
+s = tvm.create_schedule([x.op for x in outs])
+output_op = outs[0].op
+scheduled_ops = []
+
+def _traverse(op):
+"""Traverse operators from computation graph"""
+if op in s.outputs and tag.is_broadcast(op.tag) and len(op.axis) == 5:
+# schedule bias + bn + relu
+n, d, h, w, c = op.axis
+fused = s[op].fuse(n, d, h, w)
+s[op].parallel(fused)
+s[op].vectorize(c)
+
+if 'conv3d_ndhwc' in op.tag:
+conv = op.output(0)
+kernel = op.input_tensors[1]
+# dilation stage
+if isinstance(kernel.op, tvm.tensor.ComputeOp) and "dilate" in
kernel.op.tag:
+s[kernel].compute_inline()
+
+# padding stage
+data = op.input_tensors[0]
+data_pad = None
+if isinstance(data.op, tvm.tensor.ComputeOp) and "pad" in
data.op.tag:
+# fuse pad h and w
+data_pad = data
+data = data_pad.op.input_tensors[0]
+_, _, h_pad, w_pad, _ = data_pad.op.axis
+pad_fused = s[data_pad].fuse(h_pad, w_pad)
+s[data_pad].parallel(pad_fused)
+
+# compute conv
+C = conv
+n, d, h, w, c = s[C].op.axis
+s[C].vectorize(c)
+if op != output_op: # fuse bias + bn + activation
+_, _, _, _, c_out = output_op.axis
+s[C].compute_at(s[output_op], c_out)
+else:
+# fuse batch, depth, height axes
+fused = s[C].fuse(n, d, h)
+s[C].parallel(fused)
+
+scheduled_ops.append(op)
Review comment:
remove this line
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[GitHub] [incubator-tvm] alexgl-github commented on a change in pull request #4775: conv3d_ndhwc schedule
alexgl-github commented on a change in pull request #4775: conv3d_ndhwc schedule
URL: https://github.com/apache/incubator-tvm/pull/4775#discussion_r373714466
##
File path: topi/python/topi/x86/conv3d.py
##
@@ -0,0 +1,92 @@
+# 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, too-many-locals
+# pylint: disable=unused-argument, redefined-builtin, no-else-return
+"""Conv3D operators"""
+import tvm
+from .. import generic, tag
+
+@generic.schedule_conv3d_ndhwc.register("cpu")
+def schedule_conv3d_ndhwc(outs):
+"""TOPI schedule callback for conv3d
+
+Parameters
+--
+outs: Array of Tensor
+The computation graph description of conv3d
+in the format of an array of tensors.
+
+Returns
+---
+s: Schedule
+The computation schedule for conv3d.
+"""
+s = tvm.create_schedule([x.op for x in outs])
+output_op = outs[0].op
+scheduled_ops = []
+
+def traverse(op):
+"""Traverse operators from computation graph"""
+# inline all one-to-one-mapping operators except the last stage
(output)
+if tag.is_broadcast(op.tag):
+if op not in s.outputs:
+s[op].compute_inline()
+else: # inject custom schedule
+if len(op.axis) == 5:
+# schedule bias + bn + activation
+n, d, h, w, c = op.axis
+fused = s[op].fuse(n, d, h, w)
+s[op].parallel(fused)
+s[op].vectorize(c)
+for tensor in op.input_tensors:
+if isinstance(tensor.op, tvm.tensor.ComputeOp) and tensor.op
not in scheduled_ops:
+traverse(tensor.op)
+
+if 'conv3d_ndhwc' in op.tag:
+conv = op.output(0)
+kernel = op.input_tensors[1]
+# dilation stage
+if isinstance(kernel.op, tvm.tensor.ComputeOp) and "dilate" in
kernel.op.tag:
+s[kernel].compute_inline()
+
+# padding stage
+data = op.input_tensors[0]
+data_pad = None
+if isinstance(data.op, tvm.tensor.ComputeOp) and "pad" in
data.op.tag:
+# fuse pad h and w
+data_pad = data
+data = data_pad.op.input_tensors[0]
+_, _, h_pad, w_pad, _ = data_pad.op.axis
+pad_fused = s[data_pad].fuse(h_pad, w_pad)
+s[data_pad].parallel(pad_fused)
+
+# compute conv
+C = conv
+n, d, h, w, c = s[C].op.axis
+s[C].vectorize(c)
+if op != output_op: # fuse bias + bn + activation
+_, _, _, _, c_out = output_op.axis
+s[C].compute_at(s[output_op], c_out)
+else:
+# fuse batch, depth, height axes
+fused = s[C].fuse(n, d, h)
+s[C].parallel(fused)
+
+scheduled_ops.append(op)
+
+traverse(output_op)
Review comment:
@icemelon9 Changed to traverse_inline, thanks
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[GitHub] [incubator-tvm] yzhliu merged pull request #4795: [Relay][Topi] Use SimplifyInference for L2 Normalization.
yzhliu merged pull request #4795: [Relay][Topi] Use SimplifyInference for L2 Normalization. URL: https://github.com/apache/incubator-tvm/pull/4795 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (10f85d0 -> 90b2a1e)
This is an automated email from the ASF dual-hosted git repository. liuyizhi pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 10f85d0 Dedup BindParamByName function in VM compiler (#4793) add 90b2a1e [Relay][Topi] Use SimplifyInference for L2 Normazlization. (#4795) No new revisions were added by this update. Summary of changes: python/tvm/relay/op/nn/_nn.py | 16 src/relay/pass/pattern_util.h | 5 +++ src/relay/pass/simplify_inference.cc | 27 ++--- topi/include/topi/cuda/normalization.h | 49 --- topi/include/topi/nn/l2_normalize.h| 72 -- topi/include/topi/rocm/normalization.h | 11 -- topi/python/topi/cuda/__init__.py | 2 +- topi/python/topi/cuda/nn.py| 19 - topi/python/topi/generic/nn.py | 18 - topi/python/topi/nn/__init__.py| 1 - topi/python/topi/nn/l2_normalize.py| 45 - topi/python/topi/rocm/nn.py| 6 --- topi/src/topi.cc | 17 topi/tests/python/test_topi_l2norm.py | 63 - 14 files changed, 28 insertions(+), 323 deletions(-) delete mode 100644 topi/include/topi/nn/l2_normalize.h delete mode 100644 topi/python/topi/nn/l2_normalize.py delete mode 100644 topi/tests/python/test_topi_l2norm.py
[GitHub] [incubator-tvm] yzhliu commented on issue #4795: [Relay][Topi] Use SimplifyInference for L2 Normalization.
yzhliu commented on issue #4795: [Relay][Topi] Use SimplifyInference for L2 Normalization. URL: https://github.com/apache/incubator-tvm/pull/4795#issuecomment-580939287 Thanks @anijain2305 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on a change in pull request #4790: Fast exponent
tqchen commented on a change in pull request #4790: Fast exponent
URL: https://github.com/apache/incubator-tvm/pull/4790#discussion_r373702986
##
File path: topi/include/topi/elemwise.h
##
@@ -360,5 +359,66 @@ inline Tensor full_like(const Tensor& x,
}, name, tag);
}
+/*
+ * \brief Fast exponential function implementation
+ * log2(e^x) = x * log2(e) * log2(2) =>
+ * log2(e^x) = log2(2^(x*log2(e))) =>
+ * e^x = 2^(x*log2(e))
+ * Splitting power x*log2(e) into integer and fractional parts:
+ * e^(n+f) = e^n * e^f
+ * n = floor(x*log2(e) + 1/2)
+ * f = x - n * ln(2)
+ * exp(x) = 2^n * exp(y)
+ * Approximation for fractional part:
+ * y = exp(f) = 1 + 2 * P(x**2)/(Q(x**2) - P(x**2))
+ */
+inline Tensor fast_exp(const Tensor& _x,
+ std::string name,
+ std::string tag) {
+ auto x_hi = make_const(DataType::Float(32), 88.3762626647950f);
+ auto x_lo = make_const(DataType::Float(32), -88.3762626647949f);
+ auto log2e = make_const(DataType::Float(32), 1.44269504088896341f);
+ auto ln2 = make_const(DataType::Float(32), 0.6931471805599453f);
+ PrimExpr p[6] = {make_const(DataType::Float(32), 1.9875691500E-4f),
+ make_const(DataType::Float(32), 1.3981999507E-3f),
+ make_const(DataType::Float(32), 8.3334519073E-3f),
+ make_const(DataType::Float(32), 4.1665795894E-2f),
+ make_const(DataType::Float(32), 1.665459E-1f),
+ make_const(DataType::Float(32), 5.001201E-1f)};
+ auto one = make_const(DataType::Float(32), 1.0f);
+ auto one_half = make_const(DataType::Float(32), 0.5f);
+ auto b = make_const(DataType::Float(32), 127.0f);
+
+ return compute(_x->shape,
+ [&](const Array& i) {
+ // clamp x
+ auto x = ::tvm::max(::tvm::min(_x(i), x_hi), x_lo);
+ // integer part
+ auto n = ::tvm::floor(x * log2e + one_half);
+ // fractional part
+ auto f = x - n * ln2;
+ auto y = (p[0] * f + p[1]) * f + p[2]) * f + p[3])* f+
p[4]) * f
+ + p[5]) * f* f + f + one;
+ // Return 2^m * exp(r).
+ auto ef = tvm::reinterpret(DataType::Float(32),
+ ::tvm::cast(DataType::Int(32), n
+ b) << 23);
+ return ::tvm::max(ef * y, _x(i)); // NOLINT(*)
+ },
+ name, tag);
+}
+
+
+inline Tensor exp(const Tensor& x,
Review comment:
please add doxygen comments for the function
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[GitHub] [incubator-tvm] tqchen commented on a change in pull request #4790: Fast exponent
tqchen commented on a change in pull request #4790: Fast exponent URL: https://github.com/apache/incubator-tvm/pull/4790#discussion_r373702873 ## File path: topi/include/topi/elemwise.h ## @@ -360,5 +359,66 @@ inline Tensor full_like(const Tensor& x, }, name, tag); } +/* + * \brief Fast exponential function implementation Review comment: please add detailed comments about rguments 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4628: [Object] Add String container
tqchen commented on issue #4628: [Object] Add String container URL: https://github.com/apache/incubator-tvm/pull/4628#issuecomment-580904651 gentle ping 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 opened a new pull request #4796: [QNN] Conv2D with dilation support.
anijain2305 opened a new pull request #4796: [QNN] Conv2D with dilation support. URL: https://github.com/apache/incubator-tvm/pull/4796 Quantized SSD_VGG has a dilated conv. This PR supports better QNN lowering for symmetric dilated conv. Asymmetric dilated conv requires dilated pooling op. If we see a usecase, we can add that op. Currently, there is no major usecase. @vinx13 @FrozenGene @jackwish @yzhliu @yidawang 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] huajsj commented on a change in pull request #4791: [TOPI] upsample operator 'NCHWinic' format support.
huajsj commented on a change in pull request #4791: [TOPI] upsample operator
'NCHWinic' format support.
URL: https://github.com/apache/incubator-tvm/pull/4791#discussion_r373667310
##
File path: topi/python/topi/image/resize.py
##
@@ -18,8 +18,37 @@
"""TVM operator input resize compute."""
from __future__ import absolute_import
import tvm
+from topi.util import nchw_pack_layout, nchw_xc_layout
from .. import tag
+def get_2d_indices(indices, layout='NCHW'):
+""" Get 2d indices """
+(cc, inum, ic) = (0, 0, 0)
+if layout == 'NHWC':
+n, y, x, c = indices
+cc = None
+elif layout == 'NCHW':
+n, c, y, x = indices
+cc = None
+elif nchw_pack_layout(layout):
+n, c, y, x, inum, ic = indices
+else:
+n, c, y, x, cc = indices
+return n, c, y, x, cc, inum, ic
+
+def get_2d_pixel(data, layout, boxes, image_height, image_width, n, c, y, x,
cc, ib, ic):
+""" Get 2d pixel """
+if boxes is None:
+y = tvm.max(tvm.min(y, image_height - 1), 0)
+x = tvm.max(tvm.min(x, image_width - 1), 0)
+if layout == 'NHWC':
+return data(n, y, x, c).astype('float')
+if layout == 'NCHW':
+return data(n, c, y, x).astype('float')
+if nchw_pack_layout(layout):
+return data(n, c, y, x, ib, ic).astype('float')
+# else must be NCHWxc
Review comment:
Hi @tmoreau89 , thanks for the follow up, sure, issue just fixed.
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[GitHub] [incubator-tvm] tmoreau89 commented on a change in pull request #4791: [TOPI] upsample operator 'NCHWinic' format support.
tmoreau89 commented on a change in pull request #4791: [TOPI] upsample operator
'NCHWinic' format support.
URL: https://github.com/apache/incubator-tvm/pull/4791#discussion_r373658550
##
File path: topi/python/topi/image/resize.py
##
@@ -18,8 +18,37 @@
"""TVM operator input resize compute."""
from __future__ import absolute_import
import tvm
+from topi.util import nchw_pack_layout, nchw_xc_layout
from .. import tag
+def get_2d_indices(indices, layout='NCHW'):
+""" Get 2d indices """
+(cc, inum, ic) = (0, 0, 0)
+if layout == 'NHWC':
+n, y, x, c = indices
+cc = None
+elif layout == 'NCHW':
+n, c, y, x = indices
+cc = None
+elif nchw_pack_layout(layout):
+n, c, y, x, inum, ic = indices
+else:
+n, c, y, x, cc = indices
+return n, c, y, x, cc, inum, ic
+
+def get_2d_pixel(data, layout, boxes, image_height, image_width, n, c, y, x,
cc, ib, ic):
+""" Get 2d pixel """
+if boxes is None:
+y = tvm.max(tvm.min(y, image_height - 1), 0)
+x = tvm.max(tvm.min(x, image_width - 1), 0)
+if layout == 'NHWC':
+return data(n, y, x, c).astype('float')
+if layout == 'NCHW':
+return data(n, c, y, x).astype('float')
+if nchw_pack_layout(layout):
+return data(n, c, y, x, ib, ic).astype('float')
+# else must be NCHWxc
Review comment:
can we assert this
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[GitHub] [incubator-tvm] tmoreau89 commented on issue #4791: [TOPI] upsample operator 'NCHWinic' format support.
tmoreau89 commented on issue #4791: [TOPI] upsample operator 'NCHWinic' format support. URL: https://github.com/apache/incubator-tvm/pull/4791#issuecomment-580886430 @srkreddy1238 could you help review? 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] huajsj commented on issue #4791: [TOPI] upsample operator 'NCHWinic' format support.
huajsj commented on issue #4791: [TOPI] upsample operator 'NCHWinic' format support. URL: https://github.com/apache/incubator-tvm/pull/4791#issuecomment-580885374 Hi @jwfromm, if you have time could you help for a review? thanks. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 opened a new pull request #4795: [Relay][Topi] Use SimplifyInference for L2 Normazlization.
anijain2305 opened a new pull request #4795: [Relay][Topi] Use SimplifyInference for L2 Normazlization. URL: https://github.com/apache/incubator-tvm/pull/4795 Reason - Observed that l2_normalize (which is seemingly a sequence of element-wise op with one reduce op) was taking close to 15% of time in a ssd_vgg network. This PR converts L2 normalize to a series of Relay expr, for which we have well-defined Relay passes and topi schedules. Therefore, the PR also removes topi compute/schedules. @yidawang @yzhliu @tqchen @kazum @PariksheetPinjari909 No need of extra tests. The change is verified through existing tests. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] huajsj commented on issue #4791: [TOPI] upsample operator 'NCHWinic' format support.
huajsj commented on issue #4791: [TOPI] upsample operator 'NCHWinic' format support. URL: https://github.com/apache/incubator-tvm/pull/4791#issuecomment-580881788 Hi @tmoreau89 , this patch related to let VTA to support Yolov3, if you have time could you help for a review too? 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on issue #4787: [Relay] Conv2D padding representation
icemelon9 commented on issue #4787: [Relay] Conv2D padding representation URL: https://github.com/apache/incubator-tvm/pull/4787#issuecomment-580874675 You should also add `get_pad_tuple2d` to these contrib conv2d ops, e.g., contrib_conv2d_nchwc 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #4787: [Relay] Conv2D padding representation
icemelon9 commented on a change in pull request #4787: [Relay] Conv2D padding representation URL: https://github.com/apache/incubator-tvm/pull/4787#discussion_r373643774 ## File path: python/tvm/relay/op/nn/util.py ## @@ -0,0 +1,56 @@ +# 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 +"""NN operator common utilities""" +from __future__ import absolute_import +from import container + +def get_pad_tuple2d(padding): +"""Common code to get the pad option +Parameters +-- +padding : Union[int, Tuple[int, ...]] +Padding size +Returns +--- +pad_top : int +Padding size on top +pad_left : int +Padding size on left +pad_down : int +Padding size on down. +pad_right : int +Padding size on right. +""" +# compute the padding size +if isinstance(padding, container.Array): +padding = list(padding) +if isinstance(padding, (tuple, list)): +if len(padding) == 2: +pad_h = padding[0] * 2 +pad_w = padding[1] * 2 +elif len(padding) == 4: +return padding[0], padding[1], padding[2], padding[3] Review comment: ```suggestion return padding[0], padding[1], padding[2], padding[3] ``` 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexwong commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
alexwong commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-580873337 > Reducing all the input sizes might help with memory issues, I don't think theres any need to use big 224x224 test data. I think that would definitely help and worth a try but if single operator models are running into issues then I think larger networks would definitely as well and all of those require larger input sizes. Still looking into some ways to clean up memory. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on a change in pull request #4775: conv3d_ndhwc schedule
icemelon9 commented on a change in pull request #4775: conv3d_ndhwc schedule
URL: https://github.com/apache/incubator-tvm/pull/4775#discussion_r373643228
##
File path: topi/python/topi/x86/conv3d.py
##
@@ -0,0 +1,92 @@
+# 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, too-many-locals
+# pylint: disable=unused-argument, redefined-builtin, no-else-return
+"""Conv3D operators"""
+import tvm
+from .. import generic, tag
+
+@generic.schedule_conv3d_ndhwc.register("cpu")
+def schedule_conv3d_ndhwc(outs):
+"""TOPI schedule callback for conv3d
+
+Parameters
+--
+outs: Array of Tensor
+The computation graph description of conv3d
+in the format of an array of tensors.
+
+Returns
+---
+s: Schedule
+The computation schedule for conv3d.
+"""
+s = tvm.create_schedule([x.op for x in outs])
+output_op = outs[0].op
+scheduled_ops = []
+
+def traverse(op):
+"""Traverse operators from computation graph"""
+# inline all one-to-one-mapping operators except the last stage
(output)
+if tag.is_broadcast(op.tag):
+if op not in s.outputs:
+s[op].compute_inline()
+else: # inject custom schedule
+if len(op.axis) == 5:
+# schedule bias + bn + activation
+n, d, h, w, c = op.axis
+fused = s[op].fuse(n, d, h, w)
+s[op].parallel(fused)
+s[op].vectorize(c)
+for tensor in op.input_tensors:
+if isinstance(tensor.op, tvm.tensor.ComputeOp) and tensor.op
not in scheduled_ops:
+traverse(tensor.op)
+
+if 'conv3d_ndhwc' in op.tag:
+conv = op.output(0)
+kernel = op.input_tensors[1]
+# dilation stage
+if isinstance(kernel.op, tvm.tensor.ComputeOp) and "dilate" in
kernel.op.tag:
+s[kernel].compute_inline()
+
+# padding stage
+data = op.input_tensors[0]
+data_pad = None
+if isinstance(data.op, tvm.tensor.ComputeOp) and "pad" in
data.op.tag:
+# fuse pad h and w
+data_pad = data
+data = data_pad.op.input_tensors[0]
+_, _, h_pad, w_pad, _ = data_pad.op.axis
+pad_fused = s[data_pad].fuse(h_pad, w_pad)
+s[data_pad].parallel(pad_fused)
+
+# compute conv
+C = conv
+n, d, h, w, c = s[C].op.axis
+s[C].vectorize(c)
+if op != output_op: # fuse bias + bn + activation
+_, _, _, _, c_out = output_op.axis
+s[C].compute_at(s[output_op], c_out)
+else:
+# fuse batch, depth, height axes
+fused = s[C].fuse(n, d, h)
+s[C].parallel(fused)
+
+scheduled_ops.append(op)
+
+traverse(output_op)
Review comment:
You can use traverse_inline function to avoid redundant inlining broadcast
ops.
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[GitHub] [incubator-tvm] jwfromm commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser
jwfromm commented on issue #4497: [WIP] [Relay] Add a PyTorch to Relay Parser URL: https://github.com/apache/incubator-tvm/pull/4497#issuecomment-580865226 Reducing all the input sizes might help with memory issues, I don't think theres any need to use big 224x224 test data. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexgl-github commented on a change in pull request #4790: Fast exponent
alexgl-github commented on a change in pull request #4790: Fast exponent URL: https://github.com/apache/incubator-tvm/pull/4790#discussion_r373624561 ## File path: topi/include/topi/elemwise.h ## @@ -360,5 +359,71 @@ inline Tensor full_like(const Tensor& x, }, name, tag); } + /* + * \brief Fast exponential function implementation from Eigen + * https://github.com/eigenteam/eigen-git-mirror/blob/master/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h#L183 Review comment: Please see updated fast_exp implementation. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] comaniac commented on issue #4787: [Relay] Conv2D padding representation
comaniac commented on issue #4787: [Relay] Conv2D padding representation URL: https://github.com/apache/incubator-tvm/pull/4787#issuecomment-580836237 LGTM. @icemelon9 @yzhliu could you help review and merge? Thanks. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] inadob closed pull request #4696: [Relay][Frontend][TFlite] Add support for quantized LOGISTIC
inadob closed pull request #4696: [Relay][Frontend][TFlite] Add support for quantized LOGISTIC URL: https://github.com/apache/incubator-tvm/pull/4696 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on a change in pull request #4790: Fast exponent
tqchen commented on a change in pull request #4790: Fast exponent URL: https://github.com/apache/incubator-tvm/pull/4790#discussion_r373583565 ## File path: topi/include/topi/elemwise.h ## @@ -360,5 +359,71 @@ inline Tensor full_like(const Tensor& x, }, name, tag); } + /* + * \brief Fast exponential function implementation from Eigen + * https://github.com/eigenteam/eigen-git-mirror/blob/master/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h#L183 Review comment: The code in the main repo need to be licensed under ASv2. If the code comes from a difference license, We will need to put the code in the thirdparty and specify the license clearly. If we reference an existing algorithm and is implemented from scratch. It is better to declare ASv2 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] vizero1 opened a new pull request #4794: Change color channel from BGR to RGB for darknet preprocessing
vizero1 opened a new pull request #4794: Change color channel from BGR to RGB for darknet preprocessing URL: https://github.com/apache/incubator-tvm/pull/4794 Thanks for contributing to TVM! Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread. In the preprocessing for images in darknet we need to change the color channel from bgr to rgb. 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
