[GitHub] [incubator-tvm] kevinthesun commented on a change in pull request #4312: [TOPI][Relay][OP] Dynamic NMS and strided_slice
kevinthesun commented on a change in pull request #4312:
URL: https://github.com/apache/incubator-tvm/pull/4312#discussion_r414959052
##
File path: src/relay/op/tensor/transform.cc
##
@@ -1891,81 +1952,163 @@ Array > StridedSliceInferCorrectLayout(
}
CHECK(old_in_layouts.defined());
- CHECK_EQ(old_in_layouts.size(), 1);
+ CHECK_GE(old_in_layouts.size(), 1);
CHECK(old_in_shapes.defined());
- CHECK_EQ(old_in_shapes.size(), 1);
+ CHECK_GE(old_in_shapes.size(), 1);
auto layout = old_in_layouts[0];
if (layout.defined() && new_in_layouts.defined()) {
-CHECK_EQ(new_in_layouts.size(), 1);
+CHECK_GE(new_in_layouts.size(), 1);
auto new_layout = new_in_layouts[0];
auto shape = old_in_shapes[0];
// NOTE: Discard "const" qualifier here.
auto *params =
const_cast(attrs.as());
+CHECK(params != nullptr);
+Array begin, end, strides;
+const ConstantNode *cbegin, *cend, *cstrides;
+if ((cbegin = params->begin.as()) &&
+(cend = params->end.as()) &&
+(cstrides = params->strides.as())) {
+ int64_t* strides_val = ToVector(cstrides->data);
+ for (int64_t i = 0; i < cstrides->data.Shape().front(); ++i) {
+strides.push_back(strides_val[i]);
+ }
+ int64_t* begin_val = ToVector(cbegin->data);
+ for (int64_t i = 0; i < cbegin->data.Shape().front(); ++i) {
+begin.push_back(begin_val[i]);
+ }
+ int64_t* end_val = ToVector(cend->data);
+ for (int64_t i = 0; i < cend->data.Shape().front(); ++i) {
+end.push_back(end_val[i]);
+ }
+}
Array new_begin, new_end;
-for (size_t i = 0; i < params->begin.size(); i++) {
+for (size_t i = 0; i < begin.size(); i++) {
const LayoutAxis& axis = layout[i];
if (!axis.IsPrimal()) {
// original layout that contains splitted axes is not supported
return {{Layout::Undef()}, {Layout::Undef()}};
}
auto factor = new_layout.FactorOf(axis);
if (factor == -1) {
-new_begin.push_back(params->begin[i]);
-new_end.push_back(params->end[i]);
+new_begin.push_back(begin[i]);
+new_end.push_back(end[i]);
} else {
-if (params->strides.defined() && i < params->strides.size()) {
- auto stride = params->strides[i];
+if (strides.defined() && i < strides.size()) {
+ auto stride = strides[i];
// arbitrary stride is not supported
if (stride.defined() && stride->value != 1) {
return {{Layout::Undef()}, {Layout::Undef()}};
}
}
-int64_t begin = params->begin[i].defined() ? params->begin[i]->value :
0;
-int64_t end = params->end[i].defined() ? params->end[i]->value :
+int64_t bg = begin[i].defined() ? begin[i]->value : 0;
+int64_t ed = end[i].defined() ? end[i]->value :
shape[i].as()->value;
-if (begin % factor || end % factor) {
+if (bg % factor || ed % factor) {
// transform to original layout
return {{Layout::Undef()}, {Layout::Undef()}};
}
-new_begin.push_back(tvm::Integer(begin / factor));
-new_end.push_back(tvm::Integer(end / factor));
+new_begin.push_back(tvm::Integer(bg / factor));
+new_end.push_back(tvm::Integer(ed / factor));
}
}
-layout = new_layout;
-params->begin = new_begin;
-params->end = new_end;
- }
- return {{layout}, {layout}};
-}
+layout = new_layout;
-// Positional relay function to create StridedSlice operator used by frontend
FFI.
-Expr MakeStridedSlice(Expr data,
- Array begin,
- Array end,
- Array strides) {
- auto attrs = make_object();
- attrs->begin = std::move(begin);
- attrs->end = std::move(end);
- attrs->strides = std::move(strides);
- static const Op& op = Op::Get("strided_slice");
- return Call(op, {data}, Attrs(attrs), {});
+DLContext ctx;
+ctx.device_type = kDLCPU;
+ctx.device_id = 0;
+auto begin_ndarray = runtime::NDArray::Empty({int64_t(new_begin.size())},
+ DataType::Int(64), ctx);
+auto end_ndarray = runtime::NDArray::Empty({int64_t(new_begin.size())},
+ DataType::Int(64), ctx);
+auto strides_ndarray = runtime::NDArray::Empty({int64_t(new_begin.size())},
+ DataType::Int(64), ctx);
+int64_t* begin_data = static_cast(begin_ndarray->data);
+int64_t* end_data = static_cast(end_ndarray->data);
+for (size_t i = 0; i < new_begin.size(); ++i) {
+ begin_data[i] = new_begin[i];
+ end_data[i] = new_end[i];
+}
+params->begin = Constant(begin_ndarray);
+params->end = Constant(end_ndarray);
+ }
+ return {{layout, Layout("C"), Layout("C"), Layout("C")}, {layout}};
+}
+
+inline te::Tensor DynamicStridedSlice(const
[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414956089
##
File path: python/tvm/contrib/binutil.py
##
@@ -220,17 +228,32 @@ def tvm_callback_relocate_binary(
stack_pointer_init=stack_pointer_init)
tmp_dir = util.tempdir()
-rel_obj_path = tmp_dir.relpath("relocated.obj")
-rel_ld_script_path = tmp_dir.relpath("relocated.lds")
-with open(rel_ld_script_path, "w") as f:
+rel_obj_path = tmp_dir.relpath('relocated.obj')
+rel_ld_script_path = tmp_dir.relpath('relocate.lds')
+with open(rel_ld_script_path, 'w') as f:
f.write(ld_script_contents)
run_cmd([
-"{}ld".format(toolchain_prefix),
+'{}ld'.format(toolchain_prefix),
binary_path,
-"-T", rel_ld_script_path,
-"-o", rel_obj_path])
-with open(rel_obj_path, "rb") as f:
+'-T', rel_ld_script_path,
+'-o', rel_obj_path])
+
+with open(rel_obj_path, 'rb') as f:
rel_bin = bytearray(f.read())
+
+gdb_init_dir = os.environ.get('MICRO_GDB_INIT_DIR')
+if gdb_init_dir is not None:
+gdb_init_path = f'{gdb_init_dir}/.gdbinit'
+with open(gdb_init_path, 'r') as f:
+gdbinit_contents = f.read().split('\n')
+new_contents = []
+for line in gdbinit_contents:
+new_contents.append(line)
+if line.startswith('target'):
+new_contents.append(f'add-symbol-file {rel_obj_path}')
+with open(gdb_init_path, 'w') as f:
+f.write('\n'.join(new_contents))
Review comment:
I think that's also going to change soon, so would prefer to fix then
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[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414955744
##
File path: python/tvm/micro/base.py
##
@@ -133,44 +152,91 @@ def __exit__(self, exc_type, exc_value, exc_traceback):
self._exit()
-def create_micro_mod(c_mod, dev_config):
+def _calc_max_workspace_usage(src):
+# TODO factor in alignment to the calculation (alloc sizes will be aligned
up to the word size)
+alloc_re = re.compile(
+r'.*\* ?(.+) = (\(.+\))? TVMBackendAllocWorkspace\(.+, .+,
\(uint64_t\)(.+), .+, .+\).*')
+free_re = re.compile(r'.*if \(TVMBackendFreeWorkspace\(.+, .+,
(\(void\*\))? (.+)\) != 0\) {.*')
+max_usage = 0
+alloc_map = {}
+for line in src.split('\n'):
+if line.strip().startswith('//'):
+continue
+match = alloc_re.match(line)
+if match is not None:
+alloc_map[match.group(1)] = int(match.group(3))
+max_usage = max(max_usage, sum(alloc_map.values()))
+else:
+match = free_re.match(line)
+if match is not None:
+print(alloc_map)
+del alloc_map[match.group(2)]
+return max_usage
+
+
Review comment:
yeah more robust memory analysis will come in a follow-on. can you
explain what you mean by crash if it doesn't match the format given? I don't
know it will crash if it finds 0 allocs, it will just not catch anything
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[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414955794
##
File path: python/tvm/micro/device/host.py
##
@@ -38,59 +52,65 @@ def create_micro_lib(obj_path, src_path, lib_type,
options=None):
options : Optional[List[str]]
additional options to pass to GCC
+
+lib_src_paths : Optional[List[str]]
+paths to additional source files to be compiled into the library
"""
if options is None:
options = []
-if sys.maxsize > 2**32 and sys.platform.startswith("linux"):
-options += ["-mcmodel=large"]
+else:
+options = list(options)
+# Cannot increase optimization level on host due to code loading method.
+options.append('-O0')
Review comment:
let's just leave as is for now--the runtime will change a lot soon
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[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417: URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414955335 ## File path: python/tvm/contrib/debugger/debug_runtime.py ## @@ -181,6 +181,7 @@ def _run_debug(self): """ self.debug_datum._time_list = [ [float(t) * 1e-6] for t in self.run_individual(10, 1, 1) +#[float(t) * 1e-6] for t in self.run_individual(1, 1, 1) Review comment: done 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
[GitHub] [incubator-tvm] masahi commented on pull request #5431: misc fixes for ROCm
masahi commented on pull request #5431: URL: https://github.com/apache/incubator-tvm/pull/5431#issuecomment-619299391 Thanks @t-vi for fixing this. I recently did a clean install of rocm 3.3 and hit segfault from CodeGenLLVM destructor because of this. Now TVM + rocm is working again. 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
[GitHub] [incubator-tvm] masahi commented on pull request #5433: fix miopen padding
masahi commented on pull request #5433: URL: https://github.com/apache/incubator-tvm/pull/5433#issuecomment-619299101 Thanks @t-vi 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
[incubator-tvm] branch master updated (83930a3 -> 2dbe626)
This is an automated email from the ASF dual-hosted git repository. masahi pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 83930a3 [PYTORCH]Rsub, Embedded, OneHot ops support (#5434) add 2dbe626 fix miopen pad (#5433) No new revisions were added by this update. Summary of changes: python/tvm/relay/op/strategy/rocm.py | 4 +++- topi/python/topi/rocm/conv2d.py | 6 +++--- 2 files changed, 6 insertions(+), 4 deletions(-)
[GitHub] [incubator-tvm] yongfeng-nv commented on a change in pull request #5367: Improve IntervalSet's floormod
yongfeng-nv commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414946295
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if ((arith::is_pos_inf(max_value) && arith::is_neg_inf(min_value))
||
+(analyzer->CanProve(shape_i_min_value >= min_value) &&
+ analyzer->CanProve(shape_i_max_value <= max_value))) {
Review comment:
`tests/python/unittest/test_target_codegen_blob.py` exposes a case,
where `shape_i` is `[0, 0]` and arg_interval is `[threadIdx.y,
threadIdx.y]`,where `threadIdx.y`'s range is `[0, 7]`. Either `[0, 0]` or
`[threadIdx.y, threadIdx.y]` is fine. The current logic ends with
`[threadIdx.y, 0]`, messing up further transforms and triggering an assertion
later. We'd better to update both ends at the same time, i.e. treating
IntervalSet as an atomic object. Adding this explanation as comment in the
code.
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[GitHub] [incubator-tvm] masahi commented on pull request #5434: [PYTORCH]Rsub, Embedded, OneHot ops support
masahi commented on pull request #5434: URL: https://github.com/apache/incubator-tvm/pull/5434#issuecomment-619298153 Thanks @siju-samuel 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
[incubator-tvm] branch master updated (52bf1b3 -> 83930a3)
This is an automated email from the ASF dual-hosted git repository. masahi pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 52bf1b3 [RELAY][PYTORCH]cosh,sinh,log2,log10,log1p op support (#5395) add 83930a3 [PYTORCH]Rsub, Embedded, OneHot ops support (#5434) No new revisions were added by this update. Summary of changes: python/tvm/relay/frontend/pytorch.py | 47 tests/python/frontend/pytorch/test_forward.py | 53 +++ 2 files changed, 100 insertions(+)
[incubator-tvm] branch master updated (3cc4971 -> 52bf1b3)
This is an automated email from the ASF dual-hosted git repository. masahi pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 3cc4971 [RUNTIME][OBJECT] Introduce static slots for common objects. (#5423) add 52bf1b3 [RELAY][PYTORCH]cosh,sinh,log2,log10,log1p op support (#5395) No new revisions were added by this update. Summary of changes: python/tvm/relay/frontend/pytorch.py | 13 + python/tvm/relay/op/_tensor.py| 4 ++ python/tvm/relay/op/_tensor_grad.py | 34 ++ python/tvm/relay/op/tensor.py | 60 +++ python/tvm/relay/op/transform.py | 2 +- python/tvm/te/__init__.py | 1 + python/tvm/tir/op.py | 2 +- src/relay/op/tensor/unary.cc | 44 + src/target/intrin_rule.cc | 12 + tests/python/frontend/pytorch/test_forward.py | 25 ++ tests/python/relay/test_op_grad_level1.py | 6 ++- topi/include/topi/elemwise.h | 4 ++ topi/python/topi/math.py | 68 +++ topi/src/elemwise.cc | 20 14 files changed, 292 insertions(+), 3 deletions(-)
[GitHub] [incubator-tvm] yongfeng-nv commented on a change in pull request #5367: Improve IntervalSet's floormod
yongfeng-nv commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414944483
##
File path: include/tvm/arith/analyzer.h
##
@@ -411,8 +412,9 @@ class TVM_DLL Analyzer {
*
* \param var The variable.
* \param expr The expression we bind to.
+ * \param override Whether do we allow override of existing information.
Review comment:
Rephrasing the comments. Thanks
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[GitHub] [incubator-tvm] yongfeng-nv commented on a change in pull request #5367: Improve IntervalSet's floormod
yongfeng-nv commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414940958
##
File path: src/arith/int_set.cc
##
@@ -311,6 +311,16 @@ inline IntervalSet Combine(Analyzer*
analyzer,
LOG(FATAL) << "Modular by zero in CombineInterval Mod";
}
if (analyzer->CanProveGreaterEqual(divisor, 0)) {
+ if (b->min_value.as()) {
Review comment:
Make sense. I will make the change.
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[GitHub] [incubator-tvm] tmoreau89 opened a new pull request #5442: [Docs] VTA install doc migration from md to rst
tmoreau89 opened a new pull request #5442: URL: https://github.com/apache/incubator-tvm/pull/5442 Addresses issue #5396 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
[GitHub] [incubator-tvm] roastduck commented on pull request #5382: [TE] Fix MakeLoopNest for warp memory
roastduck commented on pull request #5382: URL: https://github.com/apache/incubator-tvm/pull/5382#issuecomment-619291622 Do you mean requiring the users to tag the iter_var or we do it in InferBound? If the former, maybe we can merge this PR first and then start an RFC for the API change. If the latter, can you point out where to tag the iter_var? I am not familiar with InferBound. 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
[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414919556
##
File path: topi/python/topi/arm_cpu/cortex_m7/micro_kernel/gemm.py
##
@@ -0,0 +1,221 @@
+# 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, no-value-for-parameter
+"""Defines gemm intrinsics for SIMD matrix multiplication."""
+
+import random
+import string
+
+import tvm
+from tvm import te
+
+##
+# MxKxN MatMul Intrinsic #
+##
+
+# NOTE this is transposed matmul (A * B^T)
+def intrin_gemm_MxKxN(M, K, N, in_dtype, out_dtype):
+"""Defines a SIMD-accelerated transposed matmul."""
+# we generate a unique ID for every intrinsic definition, to prevent name
+# collisions in the generated source (e.g., if there are multiple operators
+# in the same module that use the same intrinsic)
+#
+# TODO to cut down on memory usage, we should cache each intrinsic
+# instantiation and include it only once, eliminating the need for unique
+# IDs
+UNIQ_ID_LEN = 8
+uniq_id = ''.join(random.choices(string.ascii_uppercase, k=UNIQ_ID_LEN))
+
+if isinstance(M, tvm.tir.IntImm):
+M = M.value
+if isinstance(K, tvm.tir.IntImm):
+K = K.value
+if isinstance(N, tvm.tir.IntImm):
+N = N.value
+assert K % 4 == 0
+# TODO support more dtypes?
+assert in_dtype == 'int8'
+assert out_dtype == 'int32'
+A = te.placeholder((M, K), name='a', dtype=in_dtype)
+B = te.placeholder((N, K), name='b', dtype=in_dtype)
+k = te.reduce_axis((0, K), name='k')
+C = te.compute(
+(M, N),
+lambda i, j: te.sum(A[i, k].astype(out_dtype) * B[j,
k].astype(out_dtype), axis=k),
+name='c')
+A_buf = tvm.tir.decl_buffer(
+A.shape, A.dtype,
+name="A",
+offset_factor=1,
+strides=[te.var("A_s"), 1])
+B_buf = tvm.tir.decl_buffer(
+B.shape, B.dtype,
+name="B",
+offset_factor=1,
+strides=[te.var("B_s"), 1])
+C_buf = tvm.tir.decl_buffer(
+C.shape, C.dtype,
+name="C",
+offset_factor=1,
+strides=[te.var("C_s"), 1])
+def intrin_func(ins, outs):
+aa, bb = ins
+cc = outs[0]
+def _reduce_update():
+ib = tvm.tir.ir_builder.create()
+ib.emit(tvm.tir.call_extern("int32",
f"gemm_{M}x{K}x{N}_update_{uniq_id}",
+aa.access_ptr("r"),
+bb.access_ptr("r"),
+cc.access_ptr("w"),
+aa.strides[0],
+bb.strides[0],
+cc.strides[0]))
+return ib.get()
+def _reduce_reset():
+ib = tvm.tir.ir_builder.create()
+ib.emit(tvm.tir.call_extern("int32",
f"gemm_{M}x{K}x{N}_reset_{uniq_id}",
+cc.access_ptr("w"),
+cc.strides[0]))
+return ib.get()
+def _body():
+ib = tvm.tir.ir_builder.create()
+# # NOTE we need the reset in the body for cases where the buffer
+# # we're accumulating into is uninitialized (e.g., if it's the
+# # result of a workspace allocation, because there are no
guarantees
+# # on the contents).
+# ib.emit(tvm.tir.call_extern("int32", f"gemm_{M}x{K}x{N}_reset",
+# cc.access_ptr("w"),
+# cc.strides[0]))
+# ib.emit(tvm.tir.call_extern("int32", f"gemm_{M}x{K}x{N}_update",
+# aa.access_ptr("r"),
+# bb.access_ptr("r"),
+# cc.access_ptr("w"),
+# aa.strides[0],
+# bb.strides[0],
+# cc.strides[0]))
+ib.emit(tvm.tir.call_extern("int32",
f"gemm_{M}x{K}x{N}_body_{uniq_id}",
+
[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417: URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414919196 ## File path: tests/python/unittest/test_runtime_micro.py ## @@ -25,8 +25,25 @@ from tvm.micro import create_micro_mod from tvm.relay.testing import resnet -# Use the host emulated micro device. -DEV_CONFIG = micro.device.host.default_config() +# # Use the host emulated micro device. +DEV_CONFIG_A = micro.device.host.generate_config() +DEV_CONFIG_B = micro.device.host.generate_config() +TARGET = 'c -device=micro_dev' Review comment: we still have test_interleave_sessions for now though? 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
[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417: URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414919333 ## File path: topi/python/topi/arm_cpu/conv2d_spatial_pack.py ## @@ -173,7 +173,7 @@ def schedule_conv2d_spatial_pack_nchw(cfg, s, data_vec, kernel_vec, axis_lens=[cfg['tile_oh'].size[-1], cfg['tile_ow'].size[-1], cfg['tile_co'].size[-1]], - max_unroll=16, + max_unroll=None, Review comment: done 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
[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414918682
##
File path: src/runtime/micro/openocd_low_level_device.cc
##
@@ -210,9 +210,9 @@ class OpenOCDLowLevelDevice final : public LowLevelDevice {
// NOTE: OpenOCD will call any request larger than this constant an "absurd
// request".
/*! \brief maximum number of bytes allowed in a single memory transfer */
- static const constexpr ssize_t kMemTransferLimit = 64000;
+ static const constexpr ssize_t kMemTransferLimit = 8000;
Review comment:
I can't remember anymore exactly where this limit hits, but iirc it's
due to mac os x pipe buffering. I think it's because we are reading the pipe
line by line on the TVM side, but if you issue a memory transfer that prints
more than ~24k of characters, the os pipe buffer fills up before the newline
char is sent and we deadlock. updated comment.
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[GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414918275
##
File path: src/runtime/micro/micro_session.cc
##
@@ -489,6 +629,16 @@ PackedFunc MicroSession::GetFunction(
return PackedFunc([sptr_to_self](TVMArgs args, TVMRetValue* rv) {
MicroSession::ExitWithScope();
});
+// TODO(weberlo): add a `clear_batch_timer` func
+ } else if (name == "get_last_batch_time") {
+return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+ *rv = this->GetLastBatchTime();
+});
+// TODO(weberlo): remove this func
+ } else if (name == "get_last_batch_cycles") {
+return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+ *rv = this->GetLastBatchCycles();
+});
Review comment:
right now it returns either last host or last device time based on
`use_device_timer`. I agree we should rethink the timing API, but perhaps we
can move it to next PR, when I would want to implement an API between the host
and device, and we can better define the concept of batch time (and the units
it is logged in) there?
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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5441: Add TopK to ONNX Frontend
mbrookhart commented on a change in pull request #5441:
URL: https://github.com/apache/incubator-tvm/pull/5441#discussion_r414914928
##
File path: python/tvm/relay/frontend/onnx.py
##
@@ -1470,6 +1470,23 @@ def _impl_v9(cls, inputs, attr, params):
output = AttrCvt(op_name='argwhere')(inputs, attr, params)
return _op.transpose(output, axes=(1, 0))
+class TopK(OnnxOpConverter):
+"""Operator converter for TopK
+"""
+@classmethod
+def _impl_v1(cls, inputs, attr, params):
+if len(inputs) != 2:
+raise ValueError("Expect 2 input only")
+inp = inputs[0]
+axis = attr.get("axis", len(infer_shape(inp)) - 1)
+largest = attr.get("largest", 1)
+
+if largest == 0:
+raise ValueError("TVM only supports finding TopK largest elements")
Review comment:
It's a boolean value stored as an int. Anything but zero is technically
true, and I only want to throw on the false case.
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[GitHub] [incubator-tvm] weberlo commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
weberlo commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r414765031
##
File path: python/tvm/autotvm/tuner/tuner.py
##
@@ -150,7 +150,15 @@ def tune(self, n_trial, measure_option,
early_stopping=None, callbacks=(), si_pr
i + k + 1, si_prefix, format_si_prefix(flops,
si_prefix),
format_si_prefix(self.best_flops, si_prefix),
res, config)
-i += len(results)
+num_successes = 0
+for result in results:
+if isinstance(result.costs[0], float):
+num_successes += 1
+if num_successes != len(results):
+logger.debug('not counting %d failures towards trial count',
+ len(results) - num_successes)
+i += num_successes
+
Review comment:
we should probably remove this for the time being. I added this when
µTVM autotuning support was still pretty unstable, because there were _a lot_
of failed trials
##
File path: python/tvm/contrib/binutil.py
##
@@ -220,17 +228,32 @@ def tvm_callback_relocate_binary(
stack_pointer_init=stack_pointer_init)
tmp_dir = util.tempdir()
-rel_obj_path = tmp_dir.relpath("relocated.obj")
-rel_ld_script_path = tmp_dir.relpath("relocated.lds")
-with open(rel_ld_script_path, "w") as f:
+rel_obj_path = tmp_dir.relpath('relocated.obj')
+rel_ld_script_path = tmp_dir.relpath('relocate.lds')
+with open(rel_ld_script_path, 'w') as f:
f.write(ld_script_contents)
run_cmd([
-"{}ld".format(toolchain_prefix),
+'{}ld'.format(toolchain_prefix),
binary_path,
-"-T", rel_ld_script_path,
-"-o", rel_obj_path])
-with open(rel_obj_path, "rb") as f:
+'-T', rel_ld_script_path,
+'-o', rel_obj_path])
+
+with open(rel_obj_path, 'rb') as f:
rel_bin = bytearray(f.read())
+
+gdb_init_dir = os.environ.get('MICRO_GDB_INIT_DIR')
+if gdb_init_dir is not None:
+gdb_init_path = f'{gdb_init_dir}/.gdbinit'
+with open(gdb_init_path, 'r') as f:
+gdbinit_contents = f.read().split('\n')
+new_contents = []
+for line in gdbinit_contents:
+new_contents.append(line)
+if line.startswith('target'):
+new_contents.append(f'add-symbol-file {rel_obj_path}')
+with open(gdb_init_path, 'w') as f:
+f.write('\n'.join(new_contents))
Review comment:
It might be worth splitting these lines into a separate µTVM debugging
tools PR
##
File path: python/tvm/micro/device/host.py
##
@@ -38,59 +52,65 @@ def create_micro_lib(obj_path, src_path, lib_type,
options=None):
options : Optional[List[str]]
additional options to pass to GCC
+
+lib_src_paths : Optional[List[str]]
+paths to additional source files to be compiled into the library
"""
if options is None:
options = []
-if sys.maxsize > 2**32 and sys.platform.startswith("linux"):
-options += ["-mcmodel=large"]
+else:
+options = list(options)
+# Cannot increase optimization level on host due to code loading method.
+options.append('-O0')
Review comment:
`-Os` (and maybe `-O1`) work. it's just `-O2` that's been causing
problems on the host
##
File path: python/tvm/contrib/debugger/debug_runtime.py
##
@@ -181,6 +181,7 @@ def _run_debug(self):
"""
self.debug_datum._time_list = [
[float(t) * 1e-6] for t in self.run_individual(10, 1, 1)
+#[float(t) * 1e-6] for t in self.run_individual(1, 1, 1)
Review comment:
remove
##
File path: src/runtime/micro/host_driven/utvm_runtime.c
##
@@ -34,89 +34,148 @@ extern "C" {
#include "utvm_runtime.h"
-// Task pointers must be patched before calling a function.
-UTVMTask utvm_task = {
-.func = NULL,
-.arg_values = NULL,
-.arg_type_codes = NULL,
-.num_args = 0,
-};
-
-size_t utvm_word_size = 0; // NOLINT(*)
+// TODO(areusch): move defines into header
+#define TASK_QUEUE_SIZE 20
+volatile UTVMTask utvm_tasks[TASK_QUEUE_SIZE] = { };
+volatile uint32_t utvm_num_tasks = 0;
+volatile uint32_t utvm_task_times[TASK_QUEUE_SIZE] = { };
// These pointers are patched at load time to point to the workspace section.
-char* utvm_workspace_start = NULL; // NOLINT(*)
-char* utvm_workspace_end = NULL;// NOLINT(*)
-char* utvm_workspace_curr = NULL; // NOLINT(*)
+volatile char* utvm_workspace_start = NULL; // NOLINT(*)
+volatile char* utvm_workspace_end = NULL;// NOLINT(*)
+volatile char* utvm_workspace_curr = NULL; // NOLINT(*)
+#define MAX_WS_ALLOCS 10
+volatile char* utvm_alloc_ends[MAX_WS_ALLOCS] = {}; // NOLINT(*)
+volatile uint32_t utvm_alloc_idx = 0;
// Keep track of how many active allocations there are on the
[GitHub] [incubator-tvm] yzhliu commented on issue #3670: [RFC] AlterOpLayout Pass Refactoring
yzhliu commented on issue #3670: URL: https://github.com/apache/incubator-tvm/issues/3670#issuecomment-619263443 @mbrookhart not at all, please feel free to proceed :) 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
[GitHub] [incubator-tvm] hcho3 commented on issue #4953: [CI][Docker] xgboost 1.0.1 causes segfault on test_autotvm_xgboost_model.py
hcho3 commented on issue #4953: URL: https://github.com/apache/incubator-tvm/issues/4953#issuecomment-619255802 @leandron @areusch @tqchen I've put up RC1 for the upcoming XGBoost 1.1.0 release. Feel free to try it: ``` python3 -m pip install xgboost==1.1.0rc1 ``` The unit test should not crash. 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
[GitHub] [incubator-tvm] mbrookhart commented on issue #3670: [RFC] AlterOpLayout Pass Refactoring
mbrookhart commented on issue #3670: URL: https://github.com/apache/incubator-tvm/issues/3670#issuecomment-619253852 @yzhliu I've hit a couple of issue that would be completely solved by this change :). Any chance you've made progress? If not, would you be offended if I gave it a shot over the next few weeks? 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
[GitHub] [incubator-tvm] yzhliu commented on a change in pull request #5367: Improve IntervalSet's floormod
yzhliu commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414870644
##
File path: include/tvm/arith/analyzer.h
##
@@ -411,8 +412,9 @@ class TVM_DLL Analyzer {
*
* \param var The variable.
* \param expr The expression we bind to.
+ * \param override Whether do we allow override of existing information.
Review comment:
```suggestion
* \param override Whether do we allow override of existing binding
variables.
```
suggest to be more specific.
##
File path: src/arith/int_set.cc
##
@@ -311,6 +311,16 @@ inline IntervalSet Combine(Analyzer*
analyzer,
LOG(FATAL) << "Modular by zero in CombineInterval Mod";
}
if (analyzer->CanProveGreaterEqual(divisor, 0)) {
+ if (b->min_value.as()) {
Review comment:
use `divisor` to keep consistent?
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if ((arith::is_pos_inf(max_value) && arith::is_neg_inf(min_value))
||
+(analyzer->CanProve(shape_i_min_value >= min_value) &&
+ analyzer->CanProve(shape_i_max_value <= max_value))) {
Review comment:
could you elaborate a bit why do we need this change?
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[GitHub] [incubator-tvm] jwfromm commented on a change in pull request #5441: Add TopK to ONNX Frontend
jwfromm commented on a change in pull request #5441:
URL: https://github.com/apache/incubator-tvm/pull/5441#discussion_r414874263
##
File path: python/tvm/relay/frontend/onnx.py
##
@@ -1470,6 +1470,23 @@ def _impl_v9(cls, inputs, attr, params):
output = AttrCvt(op_name='argwhere')(inputs, attr, params)
return _op.transpose(output, axes=(1, 0))
+class TopK(OnnxOpConverter):
+"""Operator converter for TopK
+"""
+@classmethod
+def _impl_v1(cls, inputs, attr, params):
+if len(inputs) != 2:
+raise ValueError("Expect 2 input only")
+inp = inputs[0]
+axis = attr.get("axis", len(infer_shape(inp)) - 1)
+largest = attr.get("largest", 1)
+
+if largest == 0:
+raise ValueError("TVM only supports finding TopK largest elements")
Review comment:
maybe assert that `largest > 0` to catch negative cases as well.
##
File path: python/tvm/relay/frontend/onnx.py
##
@@ -1470,6 +1470,23 @@ def _impl_v9(cls, inputs, attr, params):
output = AttrCvt(op_name='argwhere')(inputs, attr, params)
return _op.transpose(output, axes=(1, 0))
+class TopK(OnnxOpConverter):
+"""Operator converter for TopK
+"""
+@classmethod
+def _impl_v1(cls, inputs, attr, params):
+if len(inputs) != 2:
+raise ValueError("Expect 2 input only")
+inp = inputs[0]
+axis = attr.get("axis", len(infer_shape(inp)) - 1)
Review comment:
why not use `-1` as axis instead of inferring shape here?
##
File path: tests/python/frontend/onnx/test_forward.py
##
@@ -2330,6 +2330,44 @@ def verify_nonzero(indata, outdata, dtype):
result = np.array((np.nonzero(input_data))) # expected output [[0, 1, 2,
2], [0, 1, 0, 1]]
verify_nonzero(input_data, result, dtype=np.int64)
+def test_topk():
+def verify_topk(input_dims, K, axis=-1):
+output_dims = list(input_dims)
+output_dims[axis] = K
+
+node = helper.make_node('TopK',
+inputs=['X', 'K'],
+outputs=['Values', 'Indicies'],
+axis=axis)
+
+graph = helper.make_graph([node],
+ "topk_test",
+ inputs=[helper.make_tensor_value_info("X",
TensorProto.FLOAT, list(input_dims)),
+ helper.make_tensor_value_info("K",
TensorProto.INT64, [1,])],
+ initializer=[helper.make_tensor("K",
TensorProto.INT64, [1], [K])],
+
outputs=[helper.make_tensor_value_info("Values", TensorProto.FLOAT,
output_dims),
+
helper.make_tensor_value_info("Indicies", TensorProto.INT64, output_dims)])
+
+model = helper.make_model(graph, producer_name='topk_test')
+
+indata = np.random.uniform(-10, 10, input_dims).astype(np.float32)
+onnx_out = get_onnxruntime_output(model, [indata, k])
+print(onnx_out)
+
+for target, ctx in [('llvm', tvm.cpu())]:
+tvm_out = get_tvm_output(model, indata, target, ctx, [output_dims,
output_dims],
+output_dtype=['float32', 'int64'])
+print(tvm_out)
+tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-05,
atol=1e-05)
+
+for shape in [[10], [10, 10], [10, 10, 10]]:
+for k in [1, 5, 10]:
+verify_topk(shape, k)
+
+verify_topk([10,10,10], 5, 0)
+verify_topk([10,10,10], 5, 1)
+verify_topk([10,10,10], 5, 2)
Review comment:
It's probably worth testing at least one other input dimension.
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[GitHub] [incubator-tvm] wpan11nv edited a comment on pull request #5382: [TE] Fix MakeLoopNest for warp memory
wpan11nv edited a comment on pull request #5382: URL: https://github.com/apache/incubator-tvm/pull/5382#issuecomment-619201349 Can we make it explicit, e.g by tagging the binding iter_var with "warp" scope. For CUDA, when the block configuration is (1, 32), threadIdx.y may be the thread var to access the warp scope 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
[GitHub] [incubator-tvm] mbrookhart opened a new pull request #5441: Add TopK to ONNX Frontend
mbrookhart opened a new pull request #5441: URL: https://github.com/apache/incubator-tvm/pull/5441 @masahi @jwfromm 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
[GitHub] [incubator-tvm] yzhliu commented on pull request #5367: Improve IntervalSet's floormod
yzhliu commented on pull request #5367: URL: https://github.com/apache/incubator-tvm/pull/5367#issuecomment-619238149 @icemelon9 is this what you're looking for? 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
[GitHub] [incubator-tvm] wpan11nv edited a comment on pull request #5382: [TE] Fix MakeLoopNest for warp memory
wpan11nv edited a comment on pull request #5382: URL: https://github.com/apache/incubator-tvm/pull/5382#issuecomment-619201349 Can we make it explicit, e.g by tagging the binding iter_var with "warp" scope. For CUDA, when the block configuration is (1, 32), threadIdx.y may "define" the warp scope. 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
[GitHub] [incubator-tvm] zhiics edited a comment on pull request #5438: [POC][IR] Initial stab at std::string->String upgrade
zhiics edited a comment on pull request #5438: URL: https://github.com/apache/incubator-tvm/pull/5438#issuecomment-619216822 @tqchen ahh, I see. I was thinking if it is necessary to have some stuff like Load/Save for tvm::String separately, so that we don't need to put them under serialization.cc. Looks it is not really needed. 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
[GitHub] [incubator-tvm] zhiics commented on pull request #5438: [POC][IR] Initial stab at std::string->String upgrade
zhiics commented on pull request #5438: URL: https://github.com/apache/incubator-tvm/pull/5438#issuecomment-619216822 @tqchen ahh, I see. I was thinking if it is necessary to have similar stuff like Load/Save for tvm::String, so that we don't need to put them under serialization.cc. Looks it is not really needed. 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
[GitHub] [incubator-tvm] michalpiszczek commented on a change in pull request #5437: [TFLite Runtime] Add TFLite Runtime dependencies to CI CPU docker build
michalpiszczek commented on a change in pull request #5437: URL: https://github.com/apache/incubator-tvm/pull/5437#discussion_r414833737 ## File path: docker/install/ubuntu_install_tflite.sh ## @@ -26,12 +26,18 @@ cd flatbuffers cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release make install -j8 cd .. -rm -rf flatbuffers # Install flatbuffers python packages. pip3 install flatbuffers pip2 install flatbuffers +# Build the TFLite static library, necessary for building with TFLite ON. +# The library is built at: +# tensorflow/tensorflow/lite/tools/make/gen/*/lib/libtensorflow-lite.a. +git clone https://github.com/tensorflow/tensorflow --branch=r2.0 Review comment: @u99127 Good catch, thank you. I noticed your PR updating the version yesterday but I forgot I was specifying 2.0 here. Will update :) 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
[GitHub] [incubator-tvm] dreamqin68 opened a new issue #5440: import vta: Cannot find config in /3rdparty/vta-hw/config/vta_config.json
dreamqin68 opened a new issue #5440:
URL: https://github.com/apache/incubator-tvm/issues/5440
I installed VTA according to the VTA Simulator Installation guide. Then I
set the environment. But when I imported vta, there was a bug.
`Traceback (most recent call last):
File "", line 1, in
File "/home/dream/tvm/vta/python/vta/__init__.py", line 27, in
from .bitstream import get_bitstream_path, download_bitstream
File "/home/dream/tvm/vta/python/vta/bitstream.py", line 24, in
from .environment import get_env
File "/home/dream/tvm/vta/python/vta/environment.py", line 325, in
Environment.current = _init_env()
File "/home/dream/tvm/vta/python/vta/environment.py", line 321, in
_init_env
raise RuntimeError("Cannot find config in %s" % str(config_path))
RuntimeError: Cannot find config in /3rdparty/vta-hw/config/vta_config.json`
In fact, I found `vta_config.json` in `/3rdparty/vta-hw/config/`.
I don't know how to fix it.
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[GitHub] [incubator-tvm] u99127 commented on a change in pull request #5437: [TFLite Runtime] Add TFLite Runtime dependencies to CI CPU docker build
u99127 commented on a change in pull request #5437: URL: https://github.com/apache/incubator-tvm/pull/5437#discussion_r414830870 ## File path: docker/install/ubuntu_install_tflite.sh ## @@ -26,12 +26,18 @@ cd flatbuffers cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release make install -j8 cd .. -rm -rf flatbuffers # Install flatbuffers python packages. pip3 install flatbuffers pip2 install flatbuffers +# Build the TFLite static library, necessary for building with TFLite ON. +# The library is built at: +# tensorflow/tensorflow/lite/tools/make/gen/*/lib/libtensorflow-lite.a. +git clone https://github.com/tensorflow/tensorflow --branch=r2.0 Review comment: why not 2.1.0 which is the latest stable version and what we install in the tree. I don’t think it’s worth the version skew. Ramana 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
[GitHub] [incubator-tvm] wpan11nv edited a comment on pull request #5428: [CODEGEN][CUDA] Fix a bug when vectorized load was involved for…
wpan11nv edited a comment on pull request #5428: URL: https://github.com/apache/incubator-tvm/pull/5428#issuecomment-619214302 Could you please add a few tests.Do you mean loading uchar2x4? We could load/store them as uint16_t x 4. I do not see similar code in PrintType below: https://github.com/apache/incubator-tvm/blob/master/src/target/source/codegen_cuda.cc#L186 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
[GitHub] [incubator-tvm] wpan11nv commented on pull request #5428: [CODEGEN][CUDA] Fix a bug when vectorized load was involved for…
wpan11nv commented on pull request #5428: URL: https://github.com/apache/incubator-tvm/pull/5428#issuecomment-619214302 Could you please add a few tests.Do you mean supporting loading uchar2x4? We could load/store them as uint16_t x 4. I do not see similar code in PrintType below: https://github.com/apache/incubator-tvm/blob/master/src/target/source/codegen_cuda.cc#L186 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5438: [POC][IR] Initial stab at std::string->String upgrade
tqchen commented on pull request #5438:
URL: https://github.com/apache/incubator-tvm/pull/5438#issuecomment-619211235
@jroesch @zhiics
wrt to the String serialization. Right now String is serialized as an normal
object. So if the content of String is printable, it will show up as
```python
{type_key: "runtime.String", repr_str: "mystring"}
```
If the content of the string is not printable, then it will show up as
```python
{type_key: "runtime.String", repr_b64: "base64encoding"}
```
So the new IR entry will look like (note that the name field actually stores
the index to the node table
```
[
{"type_key": "TypeVar", "attrs" : { "name": "2" }},
{type_key: "runtime.String", repr_str: "myname"}
]
```
This mechanism is baked into json serialization so it is a bit hard to move
out. Of course we could also say that String should mostly be printable, and
use a special serialization(e.g. just print as str when it is an old str
attribute) Thet is a special case though and makes the json serialization
harder to work with when directly serialize a String object itself.
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[incubator-tvm] branch master updated (9687307 -> 3cc4971)
This is an automated email from the ASF dual-hosted git repository. jroesch pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 9687307 Corrected TVM autotuning on GPU (#5432) add 3cc4971 [RUNTIME][OBJECT] Introduce static slots for common objects. (#5423) No new revisions were added by this update. Summary of changes: include/tvm/ir/expr.h | 7 ++-- include/tvm/ir/function.h | 1 + include/tvm/ir/tensor_type.h| 1 + include/tvm/ir/type.h | 2 ++ include/tvm/relay/expr.h| 1 + include/tvm/runtime/container.h | 6 ++-- include/tvm/runtime/ndarray.h | 4 +-- include/tvm/runtime/object.h| 43 ++--- include/tvm/runtime/packed_func.h | 2 ++ include/tvm/runtime/vm.h| 4 +-- include/tvm/tir/stmt.h | 1 + include/tvm/tir/var.h | 1 + python/tvm/relay/quantize/quantize.py | 2 +- python/tvm/runtime/container.py | 2 +- python/tvm/runtime/ndarray.py | 2 +- src/arith/canonical_simplify.cc | 1 + src/runtime/module.cc | 2 ++ src/runtime/object.cc | 34 --- src/target/codegen.cc | 1 + src/target/opt/build_cuda_on.cc | 2 +- src/target/source/codegen_opencl.cc | 2 +- src/target/source/codegen_opengl.cc | 2 +- src/target/spirv/build_vulkan.cc| 2 +- src/target/stackvm/codegen_stackvm.cc | 2 +- tests/cpp/build_module_test.cc | 8 ++--- tests/cpp/object_protocol_test.cc | 1 + tests/cpp/relay_build_module_test.cc| 14 tests/cpp/utvm_runtime_standalone_test.cc | 8 ++--- tests/python/unittest/test_te_schedule_graph.py | 6 ++-- 29 files changed, 113 insertions(+), 51 deletions(-)
[GitHub] [incubator-tvm] tqchen commented on a change in pull request #5438: [POC][IR] Initial stab at std::string->String upgrade
tqchen commented on a change in pull request #5438: URL: https://github.com/apache/incubator-tvm/pull/5438#discussion_r414821178 ## File path: python/tvm/ir/json_compact.py ## @@ -84,12 +91,26 @@ def _update_global_key(item, _): del item["global_key"] return item +def _update_from_std_str(key): +def _convert(item, nodes): +str_val = item["attrs"][key] +jdata = json.loads(tvm.ir.save_json(tvm.runtime.String(str_val))) Review comment: Can you elaborate a bit about what do you mean? Right now all tvm objects are serialized through save_json, what we are doing here is to get that particular entry and append to the end of the nodes. 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
[GitHub] [incubator-tvm] anijain2305 opened a new pull request #5439: TFlite e2e FP32 Object detection model
anijain2305 opened a new pull request #5439: URL: https://github.com/apache/incubator-tvm/pull/5439 TBD 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
[GitHub] [incubator-tvm] wpan11nv commented on pull request #5382: [TE] Fix MakeLoopNest for warp memory
wpan11nv commented on pull request #5382: URL: https://github.com/apache/incubator-tvm/pull/5382#issuecomment-619201349 Can we make it explicit, e.g by tagging the binding iter_var with "warp" scope. The existing "warp" scope attribute on stage looks odd to me. Should it be an attribute on iter_var's? For CUDA, when the block configuration is (1, 32), threadIdx.y may "define" the warp scope. 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
[GitHub] [incubator-tvm] comaniac commented on pull request #5409: [BYOC] Don't annotate constants
comaniac commented on pull request #5409: URL: https://github.com/apache/incubator-tvm/pull/5409#issuecomment-619198333 > I'm concerned that if we enforce annotation of individual constant nodes, MergeCompilerRegions will become a necessary pass to run. Otherwise we'll generate partitions just containing a ConstantNode. It seems to me that if someone runs `AnnotateTarget`, there's no reason for her to skip `MergeCompilerRegions`. If she wrote her own annotate pass, then those cases should be handled by herself. Originally I was even thinking to put `AnnotateTarget` and `MergeCompilerRegions` in the same pass, but we have consented to have separate passes so that we can have higher flexibility for the merging algorithm. > It looks like the refactor to AnnotatedRegionSet has broken this 'fix'. I think the important property to maintain is that 'every node should belong to a region' rather than 'every node should be annotated'. I agree with you that every node should belong to a region is an important property we should maintain in `AnnotateRegionSet`. I'm just concerned about treating `ConstantNode` as a special node. In this case, we have to be aware of `ConstantNode` everywhere in this infra, or we will easily introduce bugs when improving one of them otherwise. I'm hoping that we could deal with the constant nodes in `AnnotateTarget` pass and let other passes treat constant nodes as other nodes. For example like you have illustrated, this graph requires `AnnotateRegionSet` to deal with consant nodes: ``` input | begin | op -- const | end ``` but for this graph, `AnnotateRegionSet` can treat `const` as a normal op: ``` input | begin | op -- begin -- end -- const -- begin | end ``` 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
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #5438: [POC][IR] Initial stab at std::string->String upgrade
zhiics commented on a change in pull request #5438: URL: https://github.com/apache/incubator-tvm/pull/5438#discussion_r414804149 ## File path: python/tvm/ir/json_compact.py ## @@ -84,12 +91,26 @@ def _update_global_key(item, _): del item["global_key"] return item +def _update_from_std_str(key): +def _convert(item, nodes): +str_val = item["attrs"][key] +jdata = json.loads(tvm.ir.save_json(tvm.runtime.String(str_val))) Review comment: Should we bring the serialization of tvm:String out of save_json? 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
tqchen commented on pull request #5423: URL: https://github.com/apache/incubator-tvm/pull/5423#issuecomment-619192968 ping @jroesch 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
[GitHub] [incubator-tvm] yongfeng-nv commented on a change in pull request #5367: Improve IntervalSet's floormod
yongfeng-nv commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414674995
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if (arith::is_pos_inf(max_value) || arith::is_neg_inf(min_value) ||
Review comment:
No, I only mean to change a bound's ends in pair. Correcting the code.
Thank you for pointing it out.
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[GitHub] [incubator-tvm] michalpiszczek commented on pull request #5436: [TFLite Runtime] Re-enable test for remote execution via RPC
michalpiszczek commented on pull request #5436: URL: https://github.com/apache/incubator-tvm/pull/5436#issuecomment-619160168 @tmoreau89 @tqchen I've created a separate PR with just the docker changes here: https://github.com/apache/incubator-tvm/pull/5437 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
[GitHub] [incubator-tvm] tqchen edited a comment on pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
tqchen edited a comment on pull request #5423: URL: https://github.com/apache/incubator-tvm/pull/5423#issuecomment-619153621 The code still works, the overflow children will need to go through the overflow path, and call https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/object.h#L889 (which is slightly slower than bound checking). Note that the original code set all the child_slots to 0, so everything overflows (and goes to the fallback path for non-final type checking) 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
[GitHub] [incubator-tvm] tqchen edited a comment on pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
tqchen edited a comment on pull request #5423: URL: https://github.com/apache/incubator-tvm/pull/5423#issuecomment-619153621 The code still works, the overflow children will need to go through the overflow path, and call https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/object.h#L889 (which is slightly slower than bound checking). Note that the original code set all the child_slots to 0, so everything overflows. 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
tqchen commented on pull request #5423: URL: https://github.com/apache/incubator-tvm/pull/5423#issuecomment-619153621 The code still works, the overflow children will need to go through the overflow path, and call https://github.com/apache/incubator-tvm/blob/master/include/tvm/runtime/object.h#L889(which is slightly slower than bound checking). Note that the original code set all the child_slots to 0, so everything overflows. 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5438: [IR] Initial stab at std::string->String upgrade
tqchen commented on pull request #5438: URL: https://github.com/apache/incubator-tvm/pull/5438#issuecomment-619151418 cc @jroesch @icemelon9 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
[GitHub] [incubator-tvm] tqchen opened a new pull request #5438: [IR] Initial stab at std::string->String upgrade
tqchen opened a new pull request #5438: URL: https://github.com/apache/incubator-tvm/pull/5438 This PR is an example of String Update with backward compact(see json_compact.py) 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
[GitHub] [incubator-tvm] michalpiszczek opened a new pull request #5437: [TFLite Runtime] Add TFLite Runtime dependencies to CI CPU docker build
michalpiszczek opened a new pull request #5437: URL: https://github.com/apache/incubator-tvm/pull/5437 This PR contains only the docker build changes needed to re-enable the tests in: https://github.com/apache/incubator-tvm/pull/5436. 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
[GitHub] [incubator-tvm] tmoreau89 commented on pull request #5436: [TFLite Runtime] Re-enable test for remote execution via RPC
tmoreau89 commented on pull request #5436: URL: https://github.com/apache/incubator-tvm/pull/5436#issuecomment-619150720 @michalpiszczek one suggestion is just to leave the CI commented out, and scope this PR to just changing the dockerfile 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
[GitHub] [incubator-tvm] jroesch commented on pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
jroesch commented on pull request #5423: URL: https://github.com/apache/incubator-tvm/pull/5423#issuecomment-619150640 My one worry is that this is a bit fragile, what happens if the number of children changes but we forget to update the bound? 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
[GitHub] [incubator-tvm] maheshambule commented on pull request #5330: [Frontend][TFLite] support for FILL and SPLIT_V operators
maheshambule commented on pull request #5330: URL: https://github.com/apache/incubator-tvm/pull/5330#issuecomment-619145838 @u99127 I have merged the master and removed the duplicate code. 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
[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5330: [Frontend][TFLite] support for FILL and SPLIT_V operators
maheshambule commented on a change in pull request #5330:
URL: https://github.com/apache/incubator-tvm/pull/5330#discussion_r414740666
##
File path: python/tvm/relay/frontend/tflite.py
##
@@ -1067,6 +1069,27 @@ def convert_zeros_like(self, op):
return out
+def convert_fill(self, op):
+"""Convert TFLite FILL"""
+try:
+from tflite.Operator import Operator
+except ImportError:
+raise ImportError("The tflite package must be installed")
+
Review comment:
Merged master and removed.
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[GitHub] [incubator-tvm] tqchen commented on a change in pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
tqchen commented on a change in pull request #5423:
URL: https://github.com/apache/incubator-tvm/pull/5423#discussion_r414735177
##
File path: include/tvm/ir/expr.h
##
@@ -161,7 +163,8 @@ class RelayExprNode : public BaseExprNode {
template
inline const TTypeNode* type_as() const;
- static constexpr const char* _type_key = "relay.Expr";
+ static constexpr const char* _type_key = "RelayExpr";
Review comment:
This change is mainly for the Base class(given the c++ name is RelayExpr
under the root). The relay sub-classes are under the relay namespace.
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[GitHub] [incubator-tvm] jroesch commented on a change in pull request #5423: [RUNTIME][OBJECT] Introduce static slots for common objects.
jroesch commented on a change in pull request #5423:
URL: https://github.com/apache/incubator-tvm/pull/5423#discussion_r414733387
##
File path: include/tvm/ir/expr.h
##
@@ -161,7 +163,8 @@ class RelayExprNode : public BaseExprNode {
template
inline const TTypeNode* type_as() const;
- static constexpr const char* _type_key = "relay.Expr";
+ static constexpr const char* _type_key = "RelayExpr";
Review comment:
I'm not sure I like this change, its better to have the keys name spaced
to Relay imo.
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[GitHub] [incubator-tvm] mbrookhart commented on a change in pull request #5231: [POC] Pattern Language, Matcher, Rewriter, and Function Paritioner
mbrookhart commented on a change in pull request #5231: URL: https://github.com/apache/incubator-tvm/pull/5231#discussion_r414721399 ## File path: python/tvm/relay/df_pattern/__init__.py ## @@ -0,0 +1,488 @@ +# 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. +"""The Relay Pattern Language and tooling.""" +from tvm.relay import Expr +from ...ir.base import Node +from ...ir import make_node +from ...runtime import Object +from ... import _ffi as tvm_ffi +from ..op import get +from . import _ffi as ffi + + +def register_df_node(type_key=None): +"""Register a Relay node type. + +Parameters +-- +type_key : str or cls +The type key of the node. +""" +if not isinstance(type_key, str): +return tvm_ffi.register_object( +"relay.df_pattern." + type_key.__name__)(type_key) +return tvm_ffi.register_object(type_key) Review comment: @jroesch can you comment on this? This was one of your contributions to the python API. 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5432: Corrected TVM autotuning on GPU
tqchen commented on pull request #5432: URL: https://github.com/apache/incubator-tvm/pull/5432#issuecomment-619126547 Thanks @JishinMaster ! 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
[incubator-tvm] branch master updated (9a989e5 -> 9687307)
This is an automated email from the ASF dual-hosted git repository. tqchen pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 9a989e5 misc fixes for ROCm (pointer lifetime, runtime::String refactor) (#5431) add 9687307 Corrected TVM autotuning on GPU (#5432) No new revisions were added by this update. Summary of changes: python/tvm/autotvm/measure/measure_methods.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-)
[GitHub] [incubator-tvm] tqchen commented on pull request #5436: [TFLite Runtime] Re-enable test for remote execution via RPC
tqchen commented on pull request #5436: URL: https://github.com/apache/incubator-tvm/pull/5436#issuecomment-619125877 @michalpiszczek please PR the docker changes separately as we need to first build the docker binary and update them to the CI env. 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
[GitHub] [incubator-tvm] zhiics commented on issue #5427: [Relay] Lack complex tests for parser
zhiics commented on issue #5427: URL: https://github.com/apache/incubator-tvm/issues/5427#issuecomment-619123723 I think I met some other issues before with the roundtrip parser. If I remember correctly, they don't support functions in prelude 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
[GitHub] [incubator-tvm] zhiics edited a comment on issue #5427: [Relay] Lack complex tests for parser
zhiics edited a comment on issue #5427: URL: https://github.com/apache/incubator-tvm/issues/5427#issuecomment-619123723 I think I met some other issues before with the roundtrip parser. If I remember correctly, they don't support all functions in prelude 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
[GitHub] [incubator-tvm] michalpiszczek opened a new pull request #5436: [TFLite Runtime] Re-enable test for remote execution via RPC
michalpiszczek opened a new pull request #5436: URL: https://github.com/apache/incubator-tvm/pull/5436 Follow up to: https://github.com/apache/incubator-tvm/pull/5425 Changes: - Re-enables a test for remote execution with the TFLite runtime via RPC - The test for local execution is left skipped due to flakiness with reading the output tensor - The tests are made discoverable by pytest - Modifies the build steps for the CI CPU image to include dependencies needed by the TFLite runtime cc @tqchen @tmoreau89 @ZihengJiang 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5433: fix miopen padding
tqchen commented on pull request #5433: URL: https://github.com/apache/incubator-tvm/pull/5433#issuecomment-619108217 thanks @t-vi please rebase 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
[GitHub] [incubator-tvm] tqchen opened a new issue #5435: [FFI] Improve FFI Type Error Message
tqchen opened a new issue #5435: URL: https://github.com/apache/incubator-tvm/issues/5435 Currently, the PrintType for Array/Map does not try to nest into the content, but the FFI type checking do. This can create confusion when there is an error message. We should improve the TypeName printing to be able to do such kind of nesting(at least one-level of it) for more to be more informative. 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
[GitHub] [incubator-tvm] tqchen commented on issue #5427: [Relay] Lack complex tests for parser
tqchen commented on issue #5427: URL: https://github.com/apache/incubator-tvm/issues/5427#issuecomment-619099214 It would be great if we can add some of these models(or found the root cause get a fragement) and add them to the round trip test 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
[GitHub] [incubator-tvm] tqchen commented on pull request #5431: misc fixes for ROCm
tqchen commented on pull request #5431: URL: https://github.com/apache/incubator-tvm/pull/5431#issuecomment-619098492 Thansk @t-vi ! 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
[incubator-tvm] branch master updated: misc fixes for ROCm (pointer lifetime, runtime::String refactor) (#5431)
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tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new 9a989e5 misc fixes for ROCm (pointer lifetime, runtime::String
refactor) (#5431)
9a989e5 is described below
commit 9a989e57e12d38b4fe58ab22e89506f27ad5d76c
Author: Thomas Viehmann
AuthorDate: Fri Apr 24 17:56:52 2020 +0200
misc fixes for ROCm (pointer lifetime, runtime::String refactor) (#5431)
---
src/target/llvm/codegen_amdgpu.cc | 10 ++
1 file changed, 6 insertions(+), 4 deletions(-)
diff --git a/src/target/llvm/codegen_amdgpu.cc
b/src/target/llvm/codegen_amdgpu.cc
index 61121f6..8935809 100644
--- a/src/target/llvm/codegen_amdgpu.cc
+++ b/src/target/llvm/codegen_amdgpu.cc
@@ -219,8 +219,10 @@ runtime::Module BuildAMDGPU(IRModule mod, std::string
target) {
<< " -mattr=-code-object-v3 "
<< target.substr(4, target.length() - 4);
std::unique_ptr tm = GetLLVMTargetMachine(config.str());
- std::unique_ptr cg(new CodeGenAMDGPU());
std::unique_ptr ctx(new llvm::LLVMContext());
+ // careful: cg will hold a naked pointer reference to ctx, so it should
+ // have a shorter lifetime than the ctx.
+ std::unique_ptr cg(new CodeGenAMDGPU());
cg->Init("TVMAMDGPUModule", tm.get(), ctx.get(), false, false);
@@ -233,10 +235,10 @@ runtime::Module BuildAMDGPU(IRModule mod, std::string
target) {
const auto *find_rocm_bitcodes =
tvm::runtime::Registry::Get("tvm_callback_rocm_bitcode_path");
- Array bitcode_files = (*find_rocm_bitcodes)();
+ Array bitcode_files = (*find_rocm_bitcodes)();
- for (auto : bitcode_files) {
-std::string path = bitcode.as()->value;
+ for (auto _path : bitcode_files) {
+std::string path = bitcode_path;
llvm::SMDiagnostic err;
std::unique_ptr mlib = llvm::parseIRFile(path, err, *ctx);
if (mlib.get() == nullptr) {
[GitHub] [incubator-tvm] yongfeng-nv commented on a change in pull request #5367: Improve IntervalSet's floormod
yongfeng-nv commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414674995
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if (arith::is_pos_inf(max_value) || arith::is_neg_inf(min_value) ||
Review comment:
No, I only mean to change a bound's ends in pair. Correcting the code.
Thank you for point it out.
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[GitHub] [incubator-tvm] siju-samuel opened a new pull request #5434: [PYTORCH]Rsub, Embedded, OneHot ops support
siju-samuel opened a new pull request #5434: URL: https://github.com/apache/incubator-tvm/pull/5434 - rsub - embedded - one_hot #5133 @masahi please help me to review this pytorch frontend ops. 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
[GitHub] [incubator-tvm] mbaret commented on pull request #5409: [BYOC] Don't annotate constants
mbaret commented on pull request #5409: URL: https://github.com/apache/incubator-tvm/pull/5409#issuecomment-619080360 It looks like the refactor to AnnotatedRegionSet has broken this 'fix'. I think the important property to maintain is that 'every node should belong to a region' rather than 'every node should be annotated'. By this, I mean that the following structure: ``` input | begin | op -- const | end ``` while not explicitly annotating every node, should imply that the const is still in a region, just not as an input or output. If we put a begin before the const, that implies there's a data flow path into the region through the constant, which isn't strictly true. Maybe this would require a slightly modified traversal in AnnotatedRegionSet to handle this case. 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
[GitHub] [incubator-tvm] t-vi opened a new pull request #5433: fix miopen padding
t-vi opened a new pull request #5433: URL: https://github.com/apache/incubator-tvm/pull/5433 The MIOpen convolution accidentally passed the pad left + pad right (and top + bottom) instead of just the value applied to both sides. This patch fixes this, and also conditions the use of MIOpen in the op strategy on equality of the padding on the corresponding sides. 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
[GitHub] [incubator-tvm] JishinMaster opened a new pull request #5432: Corrected TVM autotuning on GPU
JishinMaster opened a new pull request #5432: URL: https://github.com/apache/incubator-tvm/pull/5432 Added missing "tir" in tvm.tir.analysis.verify_gpu_code(f, kwargs) , which made the tuning on GPU (tested with RTX2080) fail. Requesting a review from @eqy or @merrymercy 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
[GitHub] [incubator-tvm] t-vi opened a new pull request #5431: misc fixes for ROCm
t-vi opened a new pull request #5431: URL: https://github.com/apache/incubator-tvm/pull/5431 This fixes two things in the AMDGPU LLVM codegen. - pointer lifetime of the LLVM context passed to codegen as a pointer - the runtime::String refactor caused typing issues for one of the nodes 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
[GitHub] [incubator-tvm] dhruvaray commented on pull request #5430: [RELAY,TOPI][TFLITE] Sparse to dense operator - WIP
dhruvaray commented on pull request #5430: URL: https://github.com/apache/incubator-tvm/pull/5430#issuecomment-619016587 Closing this as I messed up on the rebase. Please ignore 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
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #5345: [RELAY] Move frontend utils
zhiics commented on a change in pull request #5345: URL: https://github.com/apache/incubator-tvm/pull/5345#discussion_r414572622 ## File path: python/tvm/relay/frontend/tflite.py ## @@ -2219,6 +2218,20 @@ def get_expr(self, input_tensor_idx): def has_expr(self, input_tensor_idx): return self.exp_tab.has_expr(get_tensor_name(self.subgraph, input_tensor_idx)) + +def get_scalar_from_constant(expr): +""" Returns scalar value from Relay constant scalar. """ +assert isinstance(expr, _expr.Constant) and not expr.data.shape, \ +"Expr is not a constant scalar." +value = expr.data.asnumpy() +if value.dtype == np.dtype(np.int32): +return int(value) +if value.dtype == np.dtype(np.float32): +return float(value) +assert False, "Constant expr must be float32/int32" +return None # To suppress pylint Review comment: I am okay with this type of minimal code duplication. Can we just do this for both cases? ```python assert value.dtype == np.dtype(np.int32) or value.dtype == np.dtype(np.float32), "value must be float32/int32" return np. asscalar(value) ``` 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
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #5345: [RELAY] Move frontend utils
zhiics commented on a change in pull request #5345: URL: https://github.com/apache/incubator-tvm/pull/5345#discussion_r414572622 ## File path: python/tvm/relay/frontend/tflite.py ## @@ -2219,6 +2218,20 @@ def get_expr(self, input_tensor_idx): def has_expr(self, input_tensor_idx): return self.exp_tab.has_expr(get_tensor_name(self.subgraph, input_tensor_idx)) + +def get_scalar_from_constant(expr): +""" Returns scalar value from Relay constant scalar. """ +assert isinstance(expr, _expr.Constant) and not expr.data.shape, \ +"Expr is not a constant scalar." +value = expr.data.asnumpy() +if value.dtype == np.dtype(np.int32): +return int(value) +if value.dtype == np.dtype(np.float32): +return float(value) +assert False, "Constant expr must be float32/int32" +return None # To suppress pylint Review comment: I am okay with this type of minimal code duplication. Can we just do for both case? ```python assert value.dtype == np.dtype(np.int32) or value.dtype == np.dtype(np.float32), "value must be float32/int32" return np. asscalar(value) ``` 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
[GitHub] [incubator-tvm] hzfan commented on a change in pull request #5367: Improve IntervalSet's floormod
hzfan commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414552082
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if (arith::is_pos_inf(max_value) || arith::is_neg_inf(min_value) ||
Review comment:
The condition seems quite loose. `max_value` gets updated when
`arith::is_neg_inf(min_value)` holds. Is this intended?
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[GitHub] [incubator-tvm] hzfan commented on a change in pull request #5367: Improve IntervalSet's floormod
hzfan commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414548737
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if (arith::is_pos_inf(max_value) || arith::is_neg_inf(min_value) ||
Review comment:
The condition seems quite loose. `max_value` gets updated when
`arith::is_neg_inf(min_value)` holds. Is this intended?
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[GitHub] [incubator-tvm] hzfan commented on a change in pull request #5367: Improve IntervalSet's floormod
hzfan commented on a change in pull request #5367:
URL: https://github.com/apache/incubator-tvm/pull/5367#discussion_r414548737
##
File path: src/te/operation/compute_op.cc
##
@@ -231,20 +231,18 @@ void ComputeOpNode::PropBoundToInputs(
// undefined behaviour), so we can intersect the estimated set of
the argument with the
// range expected by the tensor. However, intersection may result in
overly complex
// expressions, so we perform a more relaxed form of intersection.
- IntSet arg_intset = EvalSet(call->args[i], dom_map);
+ IntSet arg_intset = analyzer->int_set(call->args[i],
ConvertDomMap(dom_map));
const arith::IntervalSetNode* arg_interval =
arg_intset.as();
if (arg_interval) {
PrimExpr shape_i_min_value = make_zero(t->shape[i].dtype());
PrimExpr shape_i_max_value = t->shape[i] - 1;
PrimExpr min_value = arg_interval->min_value;
PrimExpr max_value = arg_interval->max_value;
// Prefer the shape bounds only when we can prove they are tighter.
-if (arith::is_neg_inf(min_value) ||
-analyzer->CanProve(shape_i_min_value >= min_value)) {
+if (arith::is_pos_inf(max_value) || arith::is_neg_inf(min_value) ||
Review comment:
The condition seems quite loose. `max_value` gets updated when
`arith::is_neg_inf(min_value)` holds. Is this intended?
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[GitHub] [incubator-tvm] dhruvaray opened a new pull request #5430: [RELAY,TOPI][TFLITE] Sparse to dense operator
dhruvaray opened a new pull request #5430: URL: https://github.com/apache/incubator-tvm/pull/5430 Added support for sparse_to_dense operator @u99127, @FrozenGene, @siju-samuel @jwfromm @kazum - Kindly 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
[incubator-tvm] branch master updated: [FRONTEND][TFLITE]Gather, StridedSlice op support added (#4788)
This is an automated email from the ASF dual-hosted git repository.
zhaowu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new 8efd546 [FRONTEND][TFLITE]Gather, StridedSlice op support added
(#4788)
8efd546 is described below
commit 8efd54606537411e6a3d62490b52857a411572e8
Author: Samuel
AuthorDate: Fri Apr 24 16:23:38 2020 +0530
[FRONTEND][TFLITE]Gather, StridedSlice op support added (#4788)
* [FRONTEND][TFLITE]Gather, StridedSlice op added
* Review comments fixed
---
python/tvm/relay/frontend/tflite.py | 214 +++
tests/python/frontend/tflite/test_forward.py | 75 ++
2 files changed, 289 insertions(+)
diff --git a/python/tvm/relay/frontend/tflite.py
b/python/tvm/relay/frontend/tflite.py
index 8c335c9..275d0ce 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -17,6 +17,7 @@
# pylint: disable=invalid-name, unused-argument, too-many-lines,
import-outside-toplevel
"""Tensorflow lite frontend."""
import math
+import itertools
import numpy as np
import tvm
from tvm.ir import IRModule
@@ -82,6 +83,7 @@ class OperatorConverter(object):
'FLOOR_MOD': self.convert_floor_mod,
'FLOOR': self.convert_floor,
'FULLY_CONNECTED': self.convert_fully_connected,
+'GATHER': self.convert_gather,
'GREATER_EQUAL': self.convert_greater_equal,
'GREATER': self.convert_greater,
'HARD_SWISH': self.convert_hard_swish,
@@ -125,6 +127,7 @@ class OperatorConverter(object):
'SQUARE': self.convert_square,
'SQUARED_DIFFERENCE': self.convert_squared_difference,
'SQUEEZE': self.convert_squeeze,
+'STRIDED_SLICE': self.convert_strided_slice,
'SUB': self.convert_sub,
'SUM': self._convert_reduce_sum,
'TAN': self.convert_tan,
@@ -983,6 +986,217 @@ class OperatorConverter(object):
"""Convert tflite LOGICAL_OR"""
return self._convert_logical_binary(_op.logical_or, op)
+def convert_gather(self, op):
+"""Method to Convert TFLite GATHER operator"""
+try:
+from tflite.BuiltinOptions import BuiltinOptions
+from tflite.GatherOptions import GatherOptions
+from tflite.TensorType import TensorType
+except ImportError:
+raise ImportError("The tflite package must be installed")
+
+input_tensors = self.get_input_tensors(op)
+assert len(input_tensors) == 2, "input tensors length should be 2"
+
+data = self.get_expr(input_tensors[0].tensor_idx)
+
+indices = input_tensors[1]
+indices_type = indices.tensor.Type()
+assert indices_type in (TensorType.INT32, TensorType.INT64)
+indices_type_str = self.get_tensor_type_str(indices_type)
+indices = self.exp_tab.new_const(self.get_tensor_value(indices),
+ dtype=indices_type_str)
+
+assert op.BuiltinOptionsType() == BuiltinOptions.GatherOptions
+op_options = op.BuiltinOptions()
+gather_options = GatherOptions()
+gather_options.Init(op_options.Bytes, op_options.Pos)
+axis = gather_options.Axis()
+
+# Check the indices are with in bounds.
+data_shape = list(input_tensors[0].tensor.ShapeAsNumpy())
+data_dim = len(data_shape)
+
+axis_n = axis
+if axis_n < 0:
+axis_n += axis_n + data_dim
+assert axis_n >= 0, "Axis out of bounds"
+assert axis_n < data_dim, "Axis out of bounds"
+
+indices_val = self.get_tensor_value(input_tensors[1])
+indices_shape = list(indices_val.shape)
+indices_len = len(indices_shape)
+
+out_shape = []
+for i in range(data_dim):
+if axis_n == i:
+for j in range(indices_len):
+out_shape.append(indices_shape[j])
+else:
+out_shape.append(data_shape[i])
+
+loopover = [range(s) for s in out_shape]
+for idx in list(itertools.product(*loopover)):
+indices_position = [idx[j] for j in range(axis_n,
axis_n+indices_len)]
+
+real_indices = [idx[j] for j in range(axis_n)]
+real_indices.append(indices_val[tuple(indices_position)])
+real_indices.extend([idx[j] for j in range(axis_n + indices_len,
len(idx))])
+for r, d in zip(real_indices, data_shape):
+if r >= d:
+raise ValueError("TFLite out of bound indices are not
supported.")
+
+# Use mode 'fast' since indices are already checked within bounds.
+out = _op.take(data, indices, axis=axis, mode="fast")
+return out
+
+def convert_strided_slice(self, op):
+"""Method to Convert TFLite
[GitHub] [incubator-tvm] FrozenGene commented on pull request #4788: [FRONTEND][TFLITE]Gather, StridedSlice op support added
FrozenGene commented on pull request #4788: URL: https://github.com/apache/incubator-tvm/pull/4788#issuecomment-618942742 Thanks @siju-samuel @wyc-ruiker @u99127 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
[GitHub] [incubator-tvm] masahi commented on pull request #5383: [PYTORCH]where, addcdiv, addcmul op support
masahi commented on pull request #5383: URL: https://github.com/apache/incubator-tvm/pull/5383#issuecomment-618941150 Thanks @siju-samuel 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
[incubator-tvm] branch master updated: [PYTORCH]where, addcdiv, addcmul op support (#5383)
This is an automated email from the ASF dual-hosted git repository.
masahi pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new ba38222 [PYTORCH]where, addcdiv, addcmul op support (#5383)
ba38222 is described below
commit ba38222990feb7c2dbb18bd2e23ae7551d440fd3
Author: Samuel
AuthorDate: Fri Apr 24 16:19:26 2020 +0530
[PYTORCH]where, addcdiv, addcmul op support (#5383)
* [PYTORCH]Where, addcdiv, addcmul op support
* Review comments fixed
---
python/tvm/relay/frontend/pytorch.py | 72 +++
tests/python/frontend/pytorch/test_forward.py | 69 +
2 files changed, 110 insertions(+), 31 deletions(-)
diff --git a/python/tvm/relay/frontend/pytorch.py
b/python/tvm/relay/frontend/pytorch.py
index 0ade8af..a8eb9c4 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -279,15 +279,7 @@ def _select():
def _take():
def _impl(inputs, input_types):
data = inputs[0]
-import torch
-
-if isinstance(inputs[1], _expr.Var):
-indices = _op.cast(inputs[1], "int32")
-elif isinstance(inputs[1], torch.Tensor):
-indices = _wrap_const(inputs[1].numpy())
-else:
-msg = "Data type %s could not be parsed in take operator." %
(type(inputs[1]))
-raise AssertionError(msg)
+indices = _op.cast(inputs[1], "int32")
return _op.transform.take(data, indices=indices)
return _impl
@@ -337,6 +329,40 @@ def _repeat_interleave():
return _op.transform.repeat(data, repeats=repeats, axis=axis)
return _impl
+
+def _addcdiv():
+def _impl(inputs, input_types):
+data = inputs[0]
+c = _expr.const(inputs[3])
+t1 = inputs[1]
+t2 = inputs[2]
+
+return data + (c * (t1 / t2))
+return _impl
+
+
+def _addcmul():
+def _impl(inputs, input_types):
+data = inputs[0]
+c = _expr.const(inputs[3])
+t1 = inputs[1]
+t2 = inputs[2]
+
+return data + (c * (t1 * t2))
+return _impl
+
+
+def _where():
+def _impl(inputs, input_types):
+cond = inputs[0]
+x = inputs[1]
+y = inputs[2]
+
+return _op.where(cond, x, y)
+
+return _impl
+
+
def _ones():
def _impl(inputs, input_types):
data = inputs[0]
@@ -1382,16 +1408,7 @@ def _bitwise_not():
def _bitwise_xor():
def _impl(inputs, input_types):
lhs = inputs[0]
-
-import torch
-if isinstance(inputs[1], _expr.Var):
-rhs = inputs[1]
-elif isinstance(inputs[1], torch.Tensor):
-rhs = _wrap_const(inputs[1].numpy())
-else:
-msg = "Data type %s could not be parsed in bitwise_xor operator."
% (type(inputs[1]))
-raise AssertionError(msg)
-
+rhs = inputs[1]
lhs = _op.cast(lhs, "bool") if input_types[0] == "bool" else
_op.cast(lhs, "int")
rhs = _op.cast(rhs, "bool") if input_types[1] == "bool" else
_op.cast(rhs, "int")
@@ -1410,17 +1427,7 @@ def _logical_not():
def _logical_xor():
def _impl(inputs, input_types):
lhs = _op.cast(inputs[0], "bool")
-
-import torch
-if isinstance(inputs[1], _expr.Var):
-rhs = inputs[1]
-elif isinstance(inputs[1], torch.Tensor):
-rhs = _wrap_const(inputs[1].numpy())
-else:
-msg = "Data type %s could not be parsed in logical_xor operator."
% (type(inputs[1]))
-raise AssertionError(msg)
-
-rhs = _op.cast(rhs, "bool")
+rhs = _op.cast(inputs[1], "bool")
return _op.logical_xor(lhs, rhs)
return _impl
@@ -1551,6 +1558,8 @@ def _get_convert_map(prelude):
"aten::arange" : _arange(),
"aten::div" : _elemwise("divide"),
"aten::div_": _elemwise("divide"),
+"aten::addcdiv" : _addcdiv(),
+"aten::addcmul" : _addcmul(),
"aten::ones": _ones(),
"aten::ones_like" : _ones_like(),
"aten::zeros" : _zeros(),
@@ -1570,6 +1579,7 @@ def _get_convert_map(prelude):
"aten::split_with_sizes": _split_with_sizes(),
"aten::select" : _select(),
"aten::take": _take(),
+"aten::where" : _where(),
"aten::topk": _topk(),
"aten::relu": _relu(),
"aten::relu_" : _relu(),
@@ -1832,7 +1842,7 @@ def _get_constant(node):
tensor = node.t(attr_name)
if
[GitHub] [incubator-tvm] siju-samuel commented on pull request #5383: [PYTORCH]where, addcdiv, addcmul op support
siju-samuel commented on pull request #5383: URL: https://github.com/apache/incubator-tvm/pull/5383#issuecomment-618937603 @masahi Could you please 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
[GitHub] [incubator-tvm] lixiaoquan opened a new pull request #5429: [RELAY][TF] Support symbolic newshape for Reshape
lixiaoquan opened a new pull request #5429: URL: https://github.com/apache/incubator-tvm/pull/5429 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
[GitHub] [incubator-tvm] libaihong opened a new pull request #5428: [CODEGEN][CUDA] Fix a bug when vectorized load was involved for…
libaihong opened a new pull request #5428: URL: https://github.com/apache/incubator-tvm/pull/5428 When I test with vectorized load for char2, there is a cuda compilation error: _RuntimeError: Compilation error: /tmp/tmpa87wlle2/my_kernel.cu(3093): error: no operator "=" matches these operands operand types are: char2 = int /tmp/tmpa87wlle2/my_kernel.cu(3094): error: no operator "&" matches these operands operand types are: char2 & int /tmp/tmpa87wlle2/my_kernel.cu(3096): error: no operator ">>" matches these operands operand types are: char2 >> int /tmp/tmpa87wlle2/my_kernel.cu(3097): error: no operator ">>" matches these operands operand types are: char2 >> int_ The error occurs at the pieces of cuda codes generated by TVM as below: `char2 _66;` `_66=((signed char)(_67.x) << 0); ` `_66=_66 & ~(0x00ff << 8) |((signed char)(_67.y) << 8);` `((signed char*)T_cast)[_1.x] = ((char)(_66 >> 0));` `((signed char*)T_cast)[_1.y] = ((char)(_66 >> 8));` The “_66” is a char2 vector, but it is used as a scalar, which is the cause of this issue. However, the root cause is that the vectorized load/store for char2 is not supported. After fix this problem, the generated cuda codes are listed as following: `char2 _66;` `_66.x=((signed char)(_67.x));` `_66.y=((signed char)(_67.y)); ` `((signed char*)T_cast)[_1.x] = _66.x;` `((signed char*)T_cast)[_1.y] = _66.y;` @vinx13 , could you please help 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
[GitHub] [incubator-tvm] mbaret commented on pull request #5409: [BYOC] Don't annotate constants
mbaret commented on pull request #5409: URL: https://github.com/apache/incubator-tvm/pull/5409#issuecomment-618895831 I'm concerned that if we enforce annotation of individual constant nodes, MergeCompilerRegions will become a necessary pass to run. Otherwise we'll generate partitions just containing a ConstantNode. 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
[GitHub] [incubator-tvm] mbaret commented on pull request #5345: [RELAY] Move frontend utils
mbaret commented on pull request #5345: URL: https://github.com/apache/incubator-tvm/pull/5345#issuecomment-618893040 I couldn't find an appropriate folder to move this to, so I've instead inlined this function as it's only used in two places. 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
[GitHub] [incubator-tvm] MarisaKirisame commented on issue #5427: [Relay] Lack complex tests for parser
MarisaKirisame commented on issue #5427: URL: https://github.com/apache/incubator-tvm/issues/5427#issuecomment-618890273 Hi, in the parser test file, you can see that we use roundtrip to do test - in particular, we test that parsing/pretty printing or prettyprinting/parsing give the same result. It is very easy to add new model there. I am trying to submit to NeurIps rn, so I cant work on it. Maybe other ppl (or perhap you) can take a look? 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
[GitHub] [incubator-tvm] antinucleon opened a new issue #5427: [Relay] Lack complex tests for fromtext
antinucleon opened a new issue #5427: URL: https://github.com/apache/incubator-tvm/issues/5427 Complex tests such as full models should be added to relay parser tests. As today, I tested MobileNet / Bert and a few more models, none is able to be successfully loaded. 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
