[GitHub] [incubator-tvm] zhiics opened a new pull request #5275: [BUGFIX][IR] Fix String SEqual
zhiics opened a new pull request #5275: [BUGFIX][IR] Fix String SEqual URL: https://github.com/apache/incubator-tvm/pull/5275 cc @tqchen This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] windclarion opened a new pull request #5274: [RELAY][BYOC] for composite function, FTVMAnnotateTarget maybe do not exist
windclarion opened a new pull request #5274: [RELAY][BYOC] for composite function, FTVMAnnotateTarget maybe do not exist URL: https://github.com/apache/incubator-tvm/pull/5274 composite function needn't query OpNode's FTVMAnnotateTarget attribute. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] sergei-grechanik commented on a change in pull request #5171: [Arith] linear system and equation solver
sergei-grechanik commented on a change in pull request #5171: [Arith] linear
system and equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405262306
##
File path: src/arith/solve_linear_equation.cc
##
@@ -0,0 +1,480 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file tvm/arith/solve_linear_equation.cc
+ * \brief Solve linear equations.
+ */
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace arith {
+
+using namespace tvm::runtime;
+
+void SmithNormalFormDiag(std::vector >* S,
+ std::vector >* V,
+ std::vector* x,
+ std::vector* y) {
+ if (S->empty() || V->empty()) return;
+ size_t m = S->size();
+ size_t n = (*S)[0].size(); // n is # of variables
+ CHECK_EQ(V->size(), n);
+ CHECK_EQ((*V)[0].size(), n);
+
+ for (size_t index = 0; index < std::min(m, n); ++index) {
+// Here A is partially diagonalized, that is A[i, j] is zero for all i, j
+// such that (i < index) or (j < index), unless (i == j).
+// That is, now we are diagonalizing the submatrix with i >= index and j
>= index
+
+// Find a row with a nonzero element in the index-th column
+// (We also prefer rows where this element has minimal abs value)
+size_t best_i = index;
+for (size_t i = best_i; i < m; ++i) {
+ int64_t s_old = (*S)[best_i][index];
+ int64_t s_new = (*S)[i][index];
+ if (s_new != 0) {
+if (s_old == 0 || std::abs(s_new) < std::abs(s_old)) {
+ best_i = i;
+}
+ }
+}
+// Move the row we found to the index-th position
+std::swap((*S)[index], (*S)[best_i]);
+std::swap((*y)[index], (*y)[best_i]);
+
+// If the index-th diagonal element is still zero, try to find a column
with nonzero index-th
+// element and move it to the index-th position
+if ((*S)[index][index] == 0) {
+ for (size_t j = index + 1; j < n; ++j) {
+if ((*S)[index][j] != 0) {
+ for (size_t i = index; i < m; ++i) {
+std::swap((*S)[i][index], (*S)[i][j]);
+ }
+ // swapping columns corresponds to swapping the corresponding x
+ std::swap((*x)[index], (*x)[j]);
+ for (size_t i = 0; i < n; ++i) {
+std::swap((*V)[i][index], (*V)[i][j]);
+ }
+ break;
+}
+ }
+}
+
+// If the index-th diagonal element is still zero, then both the index-th
row and the index-th
+// column are completely zero, and we don't need to do anything; just go
to the next index
+if ((*S)[index][index] == 0) {
+ continue;
+}
+
+// Now the index-th diagonal element is non-zero and we can zero all the
index-th column
+// below it by subtracting rows from each other
+for (auto i = index + 1; i < m; ++i) {
+ if ((*S)[i][index] != 0) {
+int64_t g, a, b;
+// g = a*matrix[index][index] + b*matrix[i][index]
+if ((*S)[i][index] % (*S)[index][index] != 0) {
+ std::tie(g, a, b) = xgcd((*S)[index][index], (*S)[i][index]);
+} else {
+ // Explicitly avoid changing the index-th row. This is important to
avoid infinite loop.
+ g = (*S)[index][index];
+ a = 1;
+ b = 0;
+}
+
+// Let m = S[index][index], n = S[i][index], then the following is
true:
+//
+// [ a n/g ][ m/g n/g ] = [ 1 0 ]
+// [ b -m/g ][ b-a ] = [ 0 1 ]
+//
+// Note that the two matrices are integer (since g = gcd(m, n)).
+// We will essentially multiply our matrix on the left by a dilated
and transposed version
+// of the first of these two matrices. The second matrix is not needed
here, however we will
+// use it while zeroing the index-th row.
+
+int64_t m_g = (*S)[index][index] / g;
+int64_t n_g = (*S)[i][index] / g;
+
+// Note that j is the index of the column, not the row
+for (size_t j = index; j < (*S)[i].size(); ++j) {
+ // Multiply index-th row by a and add the i-th row multiplied by b
+ // This
[incubator-tvm] branch master updated (e9c90b7 -> 89da63e)
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 e9c90b7 [LLVM] Include Support/Host.h for declaration of getDefaultTargetTriple (#5268) add 89da63e [LINT] Remove scalalint from lint deps (#5269) No new revisions were added by this update. Summary of changes: Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-)
[GitHub] [incubator-tvm] tqchen merged pull request #5269: [LINT] Remove scalalint from lint deps
tqchen merged pull request #5269: [LINT] Remove scalalint from lint deps URL: https://github.com/apache/incubator-tvm/pull/5269 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] FrozenGene commented on issue #5230: Adding support for TFLite QnnSubtract operator.
FrozenGene commented on issue #5230: Adding support for TFLite QnnSubtract operator. URL: https://github.com/apache/incubator-tvm/pull/5230#issuecomment-610737656 > @FrozenGene If we have C = 1, then depth wise conv becomes normal conv. There is nothing to accumulate across input channels basically. And depth_multiplier becomes equal to the number of output channels. What do you think? Is the change good with you? I think it is ok. I met this thing ever. That is when input channel is 1 (for example, gray image), we will get depthwise convolution of multiplier greater than 1 rather than normal convolution. Would you mind doing one performance comparison? I expect we could get better performance when we use normal convolution. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (989b481 -> e9c90b7)
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 989b481 [PYTORCH]celu, gelu, selu activations (#5263) add e9c90b7 [LLVM] Include Support/Host.h for declaration of getDefaultTargetTriple (#5268) No new revisions were added by this update. Summary of changes: src/target/llvm/llvm_common.h | 1 + 1 file changed, 1 insertion(+)
[GitHub] [incubator-tvm] CallmeZhangChenchen commented on issue #5273: Run relay_quick_start.py Wrong
CallmeZhangChenchen commented on issue #5273: Run relay_quick_start.py Wrong URL: https://github.com/apache/incubator-tvm/issues/5273#issuecomment-610736051 and llvm no question This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (d2de35e -> 989b481)
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 d2de35e [RELAY][BYOC] Add support for composite functions in BYOC (#5261) add 989b481 [PYTORCH]celu, gelu, selu activations (#5263) No new revisions were added by this update. Summary of changes: python/tvm/relay/frontend/pytorch.py | 38 --- tests/python/frontend/pytorch/test_forward.py | 34 ++-- 2 files changed, 67 insertions(+), 5 deletions(-)
[GitHub] [incubator-tvm] masahi commented on issue #5263: [PYTORCH]celu, gelu, selu activations
masahi commented on issue #5263: [PYTORCH]celu, gelu, selu activations URL: https://github.com/apache/incubator-tvm/pull/5263#issuecomment-610735755 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 With regards, Apache Git Services
[GitHub] [incubator-tvm] CallmeZhangChenchen commented on issue #5273: Run relay_quick_start.py Wrong
CallmeZhangChenchen commented on issue #5273: Run relay_quick_start.py Wrong URL: https://github.com/apache/incubator-tvm/issues/5273#issuecomment-610735862 i think tvm is no question . This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi merged pull request #5263: [PYTORCH]celu, gelu, selu activations
masahi merged pull request #5263: [PYTORCH]celu, gelu, selu activations URL: https://github.com/apache/incubator-tvm/pull/5263 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] CallmeZhangChenchen opened a new issue #5273: Run relay_quick_start.py Wrong
CallmeZhangChenchen opened a new issue #5273: Run relay_quick_start.py Wrong URL: https://github.com/apache/incubator-tvm/issues/5273 zzjhtest@zzjhtest:~/ZCC/testtvm$ python3 relay_quick_start.py v0.0.4 def @main(%data: Tensor[(1, 3, 224, 224), float32], %bn_data_gamma: Tensor[(3), float32], %bn_data_beta: Tensor[(3), float32], %bn_data_moving_mean: Tensor[(3), float32], %bn_data_moving_var: Tensor[(3), float32], %conv0_weight: Tensor[(64, 3, 7, 7), float32], %bn0_gamma: Tensor[(64), float32], %bn0_beta: Tensor[(64), float32], %bn0_moving_mean: Tensor[(64), float32], %bn0_moving_var: Tensor[(64), float32], %stage1_unit1_bn1_gamma: Tensor[(64), float32], %stage1_unit1_bn1_beta: Tensor[(64), float32], %stage1_unit1_bn1_moving_mean: Tensor[(64), float32], %stage1_unit1_bn1_moving_var: Tensor[(64), float32], %stage1_unit1_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_bn2_gamma: Tensor[(64), float32], %stage1_unit1_bn2_beta: Tensor[(64), float32], %stage1_unit1_bn2_moving_mean: Tensor[(64), float32], %stage1_unit1_bn2_moving_var: Tensor[(64), float32], %stage1_unit1_conv2_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit1_sc_weight: Tensor[(64, 64, 1, 1), float32], %stage1_unit2_bn1_gamma: Tensor[(64), float32], %stage1_unit2_bn1_beta: Tensor[(64), float32], %stage1_unit2_bn1_moving_mean: Tensor[(64), float32], %stage1_unit2_bn1_moving_var: Tensor[(64), float32], %stage1_unit2_conv1_weight: Tensor[(64, 64, 3, 3), float32], %stage1_unit2_bn2_gamma: Tensor[(64), float32], %stage1_unit2_bn2_beta: Tensor[(64), float32], %stage1_unit2_bn2_moving_mean: Tensor[(64), float32], %stage1_unit2_bn2_moving_var: Tensor[(64), float32], %stage1_unit2_conv2_weight: Tensor[(64, 64, 3, 3), float32], %stage2_unit1_bn1_gamma: Tensor[(64), float32], %stage2_unit1_bn1_beta: Tensor[(64), float32], %stage2_unit1_bn1_moving_mean: Tensor[(64), float32], %stage2_unit1_bn1_moving_var: Tensor[(64), float32], %stage2_unit1_conv1_weight: Tensor[(128, 64, 3, 3), float32], %stage2_unit1_bn2_gamma: Tensor[(128), float32], %stage2_unit1_bn2_beta: Tensor[(128), float32], %stage2_unit1_bn2_moving_mean: Tensor[(128), float32], %stage2_unit1_bn2_moving_var: Tensor[(128), float32], %stage2_unit1_conv2_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit1_sc_weight: Tensor[(128, 64, 1, 1), float32], %stage2_unit2_bn1_gamma: Tensor[(128), float32], %stage2_unit2_bn1_beta: Tensor[(128), float32], %stage2_unit2_bn1_moving_mean: Tensor[(128), float32], %stage2_unit2_bn1_moving_var: Tensor[(128), float32], %stage2_unit2_conv1_weight: Tensor[(128, 128, 3, 3), float32], %stage2_unit2_bn2_gamma: Tensor[(128), float32], %stage2_unit2_bn2_beta: Tensor[(128), float32], %stage2_unit2_bn2_moving_mean: Tensor[(128), float32], %stage2_unit2_bn2_moving_var: Tensor[(128), float32], %stage2_unit2_conv2_weight: Tensor[(128, 128, 3, 3), float32], %stage3_unit1_bn1_gamma: Tensor[(128), float32], %stage3_unit1_bn1_beta: Tensor[(128), float32], %stage3_unit1_bn1_moving_mean: Tensor[(128), float32], %stage3_unit1_bn1_moving_var: Tensor[(128), float32], %stage3_unit1_conv1_weight: Tensor[(256, 128, 3, 3), float32], %stage3_unit1_bn2_gamma: Tensor[(256), float32], %stage3_unit1_bn2_beta: Tensor[(256), float32], %stage3_unit1_bn2_moving_mean: Tensor[(256), float32], %stage3_unit1_bn2_moving_var: Tensor[(256), float32], %stage3_unit1_conv2_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit1_sc_weight: Tensor[(256, 128, 1, 1), float32], %stage3_unit2_bn1_gamma: Tensor[(256), float32], %stage3_unit2_bn1_beta: Tensor[(256), float32], %stage3_unit2_bn1_moving_mean: Tensor[(256), float32], %stage3_unit2_bn1_moving_var: Tensor[(256), float32], %stage3_unit2_conv1_weight: Tensor[(256, 256, 3, 3), float32], %stage3_unit2_bn2_gamma: Tensor[(256), float32], %stage3_unit2_bn2_beta: Tensor[(256), float32], %stage3_unit2_bn2_moving_mean: Tensor[(256), float32], %stage3_unit2_bn2_moving_var: Tensor[(256), float32], %stage3_unit2_conv2_weight: Tensor[(256, 256, 3, 3), float32], %stage4_unit1_bn1_gamma: Tensor[(256), float32], %stage4_unit1_bn1_beta: Tensor[(256), float32], %stage4_unit1_bn1_moving_mean: Tensor[(256), float32], %stage4_unit1_bn1_moving_var: Tensor[(256), float32], %stage4_unit1_conv1_weight: Tensor[(512, 256, 3, 3), float32], %stage4_unit1_bn2_gamma: Tensor[(512), float32], %stage4_unit1_bn2_beta: Tensor[(512), float32], %stage4_unit1_bn2_moving_mean: Tensor[(512), float32], %stage4_unit1_bn2_moving_var: Tensor[(512), float32], %stage4_unit1_conv2_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit1_sc_weight: Tensor[(512, 256, 1, 1), float32], %stage4_unit2_bn1_gamma: Tensor[(512), float32], %stage4_unit2_bn1_beta: Tensor[(512), float32], %stage4_unit2_bn1_moving_mean: Tensor[(512), float32], %stage4_unit2_bn1_moving_var: Tensor[(512), float32], %stage4_unit2_conv1_weight: Tensor[(512, 512, 3, 3), float32], %stage4_unit2_bn2_gamma: Tensor[(512), float32],
[GitHub] [incubator-tvm] masahi commented on a change in pull request #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op
masahi commented on a change in pull request #5272: [BYOC] Add example of
Composite + Annotate for DNNL fused op
URL: https://github.com/apache/incubator-tvm/pull/5272#discussion_r405241238
##
File path: tests/python/relay/test_pass_partition_graph.py
##
@@ -856,6 +857,111 @@ def expected():
partitioned = transform.PartitionGraph()(mod)
assert tvm.ir.structural_equal(partitioned, ref_mod, map_free_vars=True)
+
+def test_partition_conv_bias_relu():
+def make_pattern():
+data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32"))
+weight = relay.var("weight")
+bias = relay.var("bias")
+conv = relay.nn.conv2d(data=data, weight=weight, kernel_size=(3, 3),
+ channels=8, padding=(1, 1))
+add = relay.add(conv, bias)
+return relay.nn.relu(add)
+
+def get_blocks(prefix, data, in_channel, out_channel,
+ include_bn=True, include_sigmoid=False):
+weight = relay.var(prefix + "weight")
+bn_gamma = relay.var(prefix + "bn_gamma")
+bn_beta = relay.var(prefix + "bn_beta")
+bn_mmean = relay.var(prefix + "bn_mean")
+bn_mvar = relay.var(prefix + "bn_var")
+
+layer = relay.nn.conv2d(data=data, weight=weight, kernel_size=(3, 3),
+channels=out_channel, padding=(1, 1))
+if include_bn:
+bn_output = relay.nn.batch_norm(layer, bn_gamma, bn_beta,
+bn_mmean, bn_mvar)
+layer = bn_output[0]
+if include_sigmoid:
+# dummy layer to prevent pattern detection
+layer = relay.sigmoid(layer)
+layer = relay.nn.relu(layer)
+return layer
+
+def get_net(include_bn=True, include_sigmoid=False):
+data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32"))
+layer1 = get_blocks("layer1_", data, 3, 8, include_bn, include_sigmoid)
+layer2 = get_blocks("layer2_", layer1, 8, 8, include_bn,
include_sigmoid)
+return relay.Function(relay.analysis.free_vars(layer2), layer2)
+
+def get_partitoned_mod(mod, params):
+# This is required for constant folding
+mod["main"] = bind_params_by_name(mod["main"], params)
+pattern_table = [
+("dnnl.conv_bias_relu", make_pattern())
Review comment:
Maybe it is better to move this pattern to `contrib/dnnl.py`. Let me know.
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[GitHub] [incubator-tvm] masahi commented on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens
masahi commented on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens URL: https://github.com/apache/incubator-tvm/pull/5262#issuecomment-610734684 @mbaret Is it better to merge this after https://github.com/apache/incubator-tvm/pull/5272? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi edited a comment on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens
masahi edited a comment on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens URL: https://github.com/apache/incubator-tvm/pull/5262#issuecomment-610734684 @mbaret Is it better to merge this after https://github.com/apache/incubator-tvm/pull/5272? The pattern for DNNL exists only in the test 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 With regards, Apache Git Services
[GitHub] [incubator-tvm] yzhliu commented on a change in pull request #5171: [Arith] linear system and equation solver
yzhliu commented on a change in pull request #5171: [Arith] linear system and
equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405240373
##
File path: tests/python/unittest/test_arith_solve_linear_system.py
##
@@ -14,9 +14,130 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
+import random
+import numpy as np
import tvm
-from tvm import te, arith
-from tvm.tir import ir_pass
+from tvm import te, arith, ir, tir
+
+
+def run_expr(expr, vranges):
+def _compute_body(*us):
+vmap = {v: u + r.min for (v, r), u in zip(vranges.items(), us)}
+return tir.ir_pass.Substitute(expr, vmap)
+
+A = te.compute([r.extent.value for v, r in vranges.items()], _compute_body)
+args = [tvm.nd.empty(A.shape, A.dtype)]
+sch = te.create_schedule(A.op)
+mod = tvm.build(sch, [A])
+mod(*args)
+return args[0].asnumpy()
+
+
+def check_bruteforce(bool_expr, vranges, cond=None):
+if cond is not None:
+bool_expr = te.any(tir.Not(cond), bool_expr)
+
+res = run_expr(bool_expr, vranges)
+if not np.all(res):
+indices = list(np.argwhere(res == 0)[0])
+counterex = [(str(v), i + r.min) for (v, r), i in zip(vranges.items(),
indices)]
+counterex = sorted(counterex, key=lambda x: x[0])
+counterex = ", ".join([v + " = " + str(i) for v, i in counterex])
+raise AssertionError("Expression {}\nis not true on {}\n"
+ "Counterexample: {}"
+ .format(tir.ir_pass.CanonicalSimplify(bool_expr),
vranges, counterex))
+
+
+def check_solution(solution, vranges={}):
+def _check_forward(formula1, formula2, varmap, backvarmap):
+all_vranges = vranges.copy()
+all_vranges.update({v: r for v, r in formula1.ranges.items()})
+
+# Check that the transformation is injective
+cond_on_vars = tir.const(1, 'bool')
+for v in formula1.variables:
+# variable mapping is consistent
+v_back = tir.ir_pass.Simplify(tir.ir_pass.Substitute(varmap[v],
backvarmap))
+cond_on_vars = te.all(cond_on_vars, v == v_back)
+# Also we have to check that the new relations are true when old
relations are true
+cond_subst = tir.ir_pass.Substitute(
+te.all(tir.const(1, 'bool'), *formula2.relations), backvarmap)
+# We have to include relations from vranges too
+for v in formula2.variables:
+if v in formula2.ranges:
+r = formula2.ranges[v]
+range_cond = te.all(v >= r.min, v < r.min + r.extent)
+range_cond = tir.ir_pass.Substitute(range_cond, backvarmap)
+cond_subst = te.all(cond_subst, range_cond)
+cond_subst = tir.ir_pass.Simplify(cond_subst)
+check_bruteforce(te.all(cond_subst, cond_on_vars), all_vranges,
+ cond=te.all(tir.const(1, 'bool'),
*formula1.relations))
+
+rels = solution.dst.relations
+if len(rels) == 1 and ir.structural_equal(rels[0], False):
+# not solvable, skip
+return
+_check_forward(solution.src, solution.dst,
+ solution.src_to_dst, solution.dst_to_src)
+_check_forward(solution.dst, solution.src,
+ solution.dst_to_src, solution.src_to_dst)
+
+
+def test_solution_consistency():
+random.seed(0)
Review comment:
good idea.
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[GitHub] [incubator-tvm] masahi commented on issue #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op
masahi commented on issue #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op URL: https://github.com/apache/incubator-tvm/pull/5272#issuecomment-610732150 Please review @zhiics @mbaret @comaniac @trevor-m This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #4741: [External codegen] Add test cases for fused ops with manual annotation
masahi commented on issue #4741: [External codegen] Add test cases for fused ops with manual annotation URL: https://github.com/apache/incubator-tvm/pull/4741#issuecomment-610731955 #5272 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi opened a new pull request #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op
masahi opened a new pull request #5272: [BYOC] Add example of Composite + Annotate for DNNL fused op URL: https://github.com/apache/incubator-tvm/pull/5272 This is a reimplementation of #4741 based on the new annotator support for composite added in #5261. This is the first real use case of composite in the code base. It is purely for demonstration purpose and not intended to be used for performance critical scenarios. Due to the manually inlined tensors in c codegen, full mobilenet execution is disabled in the test. I tried but it took more than 5GB of RAM and compilation didn't finish in a reasonable time. The related issue discussed in https://discuss.tvm.ai/t/external-codegen-constant-tensors-in-c-codegen/5890/ This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi closed pull request #4741: [External codegen] Add test cases for fused ops with manual annotation
masahi closed pull request #4741: [External codegen] Add test cases for fused ops with manual annotation URL: https://github.com/apache/incubator-tvm/pull/4741 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (53a4ad3 -> d2de35e)
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 53a4ad3 [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM runtime (#4459) add d2de35e [RELAY][BYOC] Add support for composite functions in BYOC (#5261) No new revisions were added by this update. Summary of changes: python/tvm/relay/transform/transform.py | 17 - src/relay/transforms/annotate_target.cc | 40 --- src/relay/transforms/merge_composite.cc | 90 - tests/python/relay/test_annotate_target.py | 46 + tests/python/relay/test_pass_merge_composite.py | 38 +++ 5 files changed, 173 insertions(+), 58 deletions(-)
[GitHub] [incubator-tvm] masahi merged pull request #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi merged pull request #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610727692 Thanks @mbaret @zhiics This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] yzhliu commented on a change in pull request #5171: [Arith] linear system and equation solver
yzhliu commented on a change in pull request #5171: [Arith] linear system and
equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405213922
##
File path: src/arith/solve_linear_equation.cc
##
@@ -0,0 +1,480 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file tvm/arith/solve_linear_equation.cc
+ * \brief Solve linear equations.
+ */
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace arith {
+
+using namespace tvm::runtime;
+
+void SmithNormalFormDiag(std::vector >* S,
+ std::vector >* V,
+ std::vector* x,
+ std::vector* y) {
+ if (S->empty() || V->empty()) return;
+ size_t m = S->size();
+ size_t n = (*S)[0].size(); // n is # of variables
+ CHECK_EQ(V->size(), n);
+ CHECK_EQ((*V)[0].size(), n);
+
+ for (size_t index = 0; index < std::min(m, n); ++index) {
+// Here A is partially diagonalized, that is A[i, j] is zero for all i, j
+// such that (i < index) or (j < index), unless (i == j).
+// That is, now we are diagonalizing the submatrix with i >= index and j
>= index
+
+// Find a row with a nonzero element in the index-th column
+// (We also prefer rows where this element has minimal abs value)
+size_t best_i = index;
+for (size_t i = best_i; i < m; ++i) {
+ int64_t s_old = (*S)[best_i][index];
+ int64_t s_new = (*S)[i][index];
+ if (s_new != 0) {
+if (s_old == 0 || std::abs(s_new) < std::abs(s_old)) {
+ best_i = i;
+}
+ }
+}
+// Move the row we found to the index-th position
+std::swap((*S)[index], (*S)[best_i]);
+std::swap((*y)[index], (*y)[best_i]);
+
+// If the index-th diagonal element is still zero, try to find a column
with nonzero index-th
+// element and move it to the index-th position
+if ((*S)[index][index] == 0) {
+ for (size_t j = index + 1; j < n; ++j) {
+if ((*S)[index][j] != 0) {
+ for (size_t i = index; i < m; ++i) {
+std::swap((*S)[i][index], (*S)[i][j]);
+ }
+ // swapping columns corresponds to swapping the corresponding x
+ std::swap((*x)[index], (*x)[j]);
+ for (size_t i = 0; i < n; ++i) {
+std::swap((*V)[i][index], (*V)[i][j]);
+ }
+ break;
+}
+ }
+}
+
+// If the index-th diagonal element is still zero, then both the index-th
row and the index-th
+// column are completely zero, and we don't need to do anything; just go
to the next index
+if ((*S)[index][index] == 0) {
+ continue;
+}
+
+// Now the index-th diagonal element is non-zero and we can zero all the
index-th column
+// below it by subtracting rows from each other
+for (auto i = index + 1; i < m; ++i) {
+ if ((*S)[i][index] != 0) {
+int64_t g, a, b;
+// g = a*matrix[index][index] + b*matrix[i][index]
+if ((*S)[i][index] % (*S)[index][index] != 0) {
+ std::tie(g, a, b) = xgcd((*S)[index][index], (*S)[i][index]);
+} else {
+ // Explicitly avoid changing the index-th row. This is important to
avoid infinite loop.
+ g = (*S)[index][index];
+ a = 1;
+ b = 0;
+}
+
+// Let m = S[index][index], n = S[i][index], then the following is
true:
+//
+// [ a n/g ][ m/g n/g ] = [ 1 0 ]
+// [ b -m/g ][ b-a ] = [ 0 1 ]
+//
+// Note that the two matrices are integer (since g = gcd(m, n)).
+// We will essentially multiply our matrix on the left by a dilated
and transposed version
+// of the first of these two matrices. The second matrix is not needed
here, however we will
+// use it while zeroing the index-th row.
+
+int64_t m_g = (*S)[index][index] / g;
+int64_t n_g = (*S)[i][index] / g;
+
+// Note that j is the index of the column, not the row
+for (size_t j = index; j < (*S)[i].size(); ++j) {
+ // Multiply index-th row by a and add the i-th row multiplied by b
+ // This will make
[GitHub] [incubator-tvm] yzhliu commented on a change in pull request #5171: [Arith] linear system and equation solver
yzhliu commented on a change in pull request #5171: [Arith] linear system and
equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405212419
##
File path: include/tvm/arith/int_solver.h
##
@@ -0,0 +1,173 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file tvm/arith/int_solver.h
+ * \brief integer constraints data structures and solvers
+ */
+#ifndef TVM_ARITH_INT_SOLVER_H_
+#define TVM_ARITH_INT_SOLVER_H_
+
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace arith {
+
+using tir::Var;
+using tir::VarNode;
+using tir::IterVar;
+
+/*!
+ * \brief Represent integer constrains including (integer) variables, their
ranges and
+ *the relations between them (either equations or inequalities).
+ * \sa LinearSystem
+ */
+class IntConstraintsNode : public Object {
+ public:
+ // e.g., \alpha, \beta, must be integers
+ Array variables;
+ // e.g., 1 <= \alpha <= N, etc.
+ Map ranges;
+ // linear equalities or inequalities
+ // e.g., A \alpha = \beta or A \alpha <= \beta
+ Array relations;
+
+ void VisitAttrs(tvm::AttrVisitor* v) {
+v->Visit("variables", );
+v->Visit("ranges", );
+v->Visit("relations", );
+ }
+
+ static constexpr const char* _type_key = "arith.IntConstraints";
+ TVM_DECLARE_FINAL_OBJECT_INFO(IntConstraintsNode, Object);
+};
+
+/*!
+ * \brief Managed reference to IntConstraintsNode.
+ * \sa IntConstraintsNode
+ */
+class IntConstraints : public ObjectRef {
+ public:
+ /*!
+ * \brief Constructor by fields
+ * \param variables The variables in the constraints, must be integers.
+ * \param rangesThe ranges of the variables.
+ * \param relations The linear relations between the variables
+ * (either equations or inequalities)
+ */
+ TVM_DLL IntConstraints(Array variables,
+ Map ranges,
+ Array relations);
+
+ TVM_DEFINE_OBJECT_REF_METHODS(IntConstraints, ObjectRef, IntConstraintsNode);
+};
+
+/*!
+ * \brief We can have different set of variables to represent the same
constraints.
+ *For example, the following two systems are equivalent,
+ *{a + b = 0 | a >= 0, b >= 0} and
+ *{m - n = 0 | m >= 0, n <= 0}
+ *This data structure represents the transformation
+ *between two equivalent linear systems.
+ *In the above example,
+ *src: {a + b = 0 | a >= 0, b >= 0}
+ *dst: {m - n = 0 | m >= 0, n <= 0}
+ *src_to_dst : {a -> m, b -> -n}
+ *dst_to_src : {m -> a, n -> -b}
+ * \sa IntConstraintsTransform
+ */
+class IntConstraintsTransformNode : public Object {
+ public:
+ IntConstraints src;
+ IntConstraints dst;
+ Map src_to_dst;
+ Map dst_to_src;
+
+ void VisitAttrs(tvm::AttrVisitor* v) {
+v->Visit("src", );
+v->Visit("dst", );
+v->Visit("src_to_dst", _to_dst);
+v->Visit("dst_to_src", _to_src);
+ }
+
+ static constexpr const char* _type_key = "arith.IntConstraintsTransform";
+ TVM_DECLARE_FINAL_OBJECT_INFO(IntConstraintsTransformNode, Object);
+};
+
+/*!
+ * \brief Managed reference to IntConstraintsTransformNode.
+ * \sa IntConstraintsTransformNode
+ */
+class IntConstraintsTransform : public ObjectRef {
+ public:
+ /*!
+ * \brief Constructor by fields
+ * \param srcsource integer constraints, e.g., {a + b = 0 | a >= 0,
b >= 0}
+ * \param dstinteger constraints equivalent to the source,
+ * e.g., {m - n = 0 | m >= 0, n <= 0}
+ * \param src_to_dst mapping from variables in the \p src to the variables
in the \p dst,
+ * e.g., {a -> m, b -> -n}
+ * \param dst_to_src mapping from variables in the \p dst to the variables
in the \p src,
+ * e.g., {m -> a, n -> -b}
+ */
+ TVM_DLL IntConstraintsTransform(IntConstraints src,
+ IntConstraints dst,
+ Map src_to_dst,
+ Map dst_to_src);
+
+ TVM_DEFINE_OBJECT_REF_METHODS(IntConstraintsTransform, ObjectRef,
IntConstraintsTransformNode);
+};
+
+/*!
+ * \brief Obtain Smith Normal Form of linear equation A
[GitHub] [incubator-tvm] sergei-grechanik commented on a change in pull request #5171: [Arith] linear system and equation solver
sergei-grechanik commented on a change in pull request #5171: [Arith] linear
system and equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405211094
##
File path: include/tvm/arith/int_solver.h
##
@@ -0,0 +1,173 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file tvm/arith/int_solver.h
+ * \brief integer constraints data structures and solvers
+ */
+#ifndef TVM_ARITH_INT_SOLVER_H_
+#define TVM_ARITH_INT_SOLVER_H_
+
+#include
+#include
+#include
+#include
+
+namespace tvm {
+namespace arith {
+
+using tir::Var;
+using tir::VarNode;
+using tir::IterVar;
+
+/*!
+ * \brief Represent integer constrains including (integer) variables, their
ranges and
+ *the relations between them (either equations or inequalities).
+ * \sa LinearSystem
+ */
+class IntConstraintsNode : public Object {
+ public:
+ // e.g., \alpha, \beta, must be integers
+ Array variables;
+ // e.g., 1 <= \alpha <= N, etc.
+ Map ranges;
+ // linear equalities or inequalities
+ // e.g., A \alpha = \beta or A \alpha <= \beta
+ Array relations;
+
+ void VisitAttrs(tvm::AttrVisitor* v) {
+v->Visit("variables", );
+v->Visit("ranges", );
+v->Visit("relations", );
+ }
+
+ static constexpr const char* _type_key = "arith.IntConstraints";
+ TVM_DECLARE_FINAL_OBJECT_INFO(IntConstraintsNode, Object);
+};
+
+/*!
+ * \brief Managed reference to IntConstraintsNode.
+ * \sa IntConstraintsNode
+ */
+class IntConstraints : public ObjectRef {
+ public:
+ /*!
+ * \brief Constructor by fields
+ * \param variables The variables in the constraints, must be integers.
+ * \param rangesThe ranges of the variables.
+ * \param relations The linear relations between the variables
+ * (either equations or inequalities)
+ */
+ TVM_DLL IntConstraints(Array variables,
+ Map ranges,
+ Array relations);
+
+ TVM_DEFINE_OBJECT_REF_METHODS(IntConstraints, ObjectRef, IntConstraintsNode);
+};
+
+/*!
+ * \brief We can have different set of variables to represent the same
constraints.
+ *For example, the following two systems are equivalent,
+ *{a + b = 0 | a >= 0, b >= 0} and
+ *{m - n = 0 | m >= 0, n <= 0}
+ *This data structure represents the transformation
+ *between two equivalent linear systems.
+ *In the above example,
+ *src: {a + b = 0 | a >= 0, b >= 0}
+ *dst: {m - n = 0 | m >= 0, n <= 0}
+ *src_to_dst : {a -> m, b -> -n}
+ *dst_to_src : {m -> a, n -> -b}
+ * \sa IntConstraintsTransform
+ */
+class IntConstraintsTransformNode : public Object {
+ public:
+ IntConstraints src;
+ IntConstraints dst;
+ Map src_to_dst;
+ Map dst_to_src;
+
+ void VisitAttrs(tvm::AttrVisitor* v) {
+v->Visit("src", );
+v->Visit("dst", );
+v->Visit("src_to_dst", _to_dst);
+v->Visit("dst_to_src", _to_src);
+ }
+
+ static constexpr const char* _type_key = "arith.IntConstraintsTransform";
+ TVM_DECLARE_FINAL_OBJECT_INFO(IntConstraintsTransformNode, Object);
+};
+
+/*!
+ * \brief Managed reference to IntConstraintsTransformNode.
+ * \sa IntConstraintsTransformNode
+ */
+class IntConstraintsTransform : public ObjectRef {
+ public:
+ /*!
+ * \brief Constructor by fields
+ * \param srcsource integer constraints, e.g., {a + b = 0 | a >= 0,
b >= 0}
+ * \param dstinteger constraints equivalent to the source,
+ * e.g., {m - n = 0 | m >= 0, n <= 0}
+ * \param src_to_dst mapping from variables in the \p src to the variables
in the \p dst,
+ * e.g., {a -> m, b -> -n}
+ * \param dst_to_src mapping from variables in the \p dst to the variables
in the \p src,
+ * e.g., {m -> a, n -> -b}
+ */
+ TVM_DLL IntConstraintsTransform(IntConstraints src,
+ IntConstraints dst,
+ Map src_to_dst,
+ Map dst_to_src);
+
+ TVM_DEFINE_OBJECT_REF_METHODS(IntConstraintsTransform, ObjectRef,
IntConstraintsTransformNode);
+};
+
+/*!
+ * \brief Obtain Smith Normal Form of linear
[GitHub] [incubator-tvm] tqchen opened a new pull request #5271: [RUNTIME] Introduce RValue reference(move) support to TypedPackedFunc
tqchen opened a new pull request #5271: [RUNTIME] Introduce RValue reference(move) support to TypedPackedFunc URL: https://github.com/apache/incubator-tvm/pull/5271 This PR introduces RValue reference support the PackedFunc calling convention to address the above issue. Specifically, when an argument is a r-value reference, we will use a assign a different type code(`kObjectRValueRefArg`), and pass `Object**` (the address to the Object pointer) instead through the values array. The callee can choose to move out this Object pointer and set the original Object pointer from the caller side to be nullptr. We also add move support to the python side. This enhancement will enable copy on write optimizations through out the TVM stack. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] sergei-grechanik commented on a change in pull request #5171: [Arith] linear system and equation solver
sergei-grechanik commented on a change in pull request #5171: [Arith] linear
system and equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405204662
##
File path: tests/python/unittest/test_arith_solve_linear_system.py
##
@@ -14,9 +14,130 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
+import random
+import numpy as np
import tvm
-from tvm import te, arith
-from tvm.tir import ir_pass
+from tvm import te, arith, ir, tir
+
+
+def run_expr(expr, vranges):
+def _compute_body(*us):
+vmap = {v: u + r.min for (v, r), u in zip(vranges.items(), us)}
+return tir.ir_pass.Substitute(expr, vmap)
+
+A = te.compute([r.extent.value for v, r in vranges.items()], _compute_body)
+args = [tvm.nd.empty(A.shape, A.dtype)]
+sch = te.create_schedule(A.op)
+mod = tvm.build(sch, [A])
+mod(*args)
+return args[0].asnumpy()
+
+
+def check_bruteforce(bool_expr, vranges, cond=None):
+if cond is not None:
+bool_expr = te.any(tir.Not(cond), bool_expr)
+
+res = run_expr(bool_expr, vranges)
+if not np.all(res):
+indices = list(np.argwhere(res == 0)[0])
+counterex = [(str(v), i + r.min) for (v, r), i in zip(vranges.items(),
indices)]
+counterex = sorted(counterex, key=lambda x: x[0])
+counterex = ", ".join([v + " = " + str(i) for v, i in counterex])
+raise AssertionError("Expression {}\nis not true on {}\n"
+ "Counterexample: {}"
+ .format(tir.ir_pass.CanonicalSimplify(bool_expr),
vranges, counterex))
+
+
+def check_solution(solution, vranges={}):
+def _check_forward(formula1, formula2, varmap, backvarmap):
+all_vranges = vranges.copy()
+all_vranges.update({v: r for v, r in formula1.ranges.items()})
+
+# Check that the transformation is injective
+cond_on_vars = tir.const(1, 'bool')
+for v in formula1.variables:
+# variable mapping is consistent
+v_back = tir.ir_pass.Simplify(tir.ir_pass.Substitute(varmap[v],
backvarmap))
+cond_on_vars = te.all(cond_on_vars, v == v_back)
+# Also we have to check that the new relations are true when old
relations are true
+cond_subst = tir.ir_pass.Substitute(
+te.all(tir.const(1, 'bool'), *formula2.relations), backvarmap)
+# We have to include relations from vranges too
+for v in formula2.variables:
+if v in formula2.ranges:
+r = formula2.ranges[v]
+range_cond = te.all(v >= r.min, v < r.min + r.extent)
+range_cond = tir.ir_pass.Substitute(range_cond, backvarmap)
+cond_subst = te.all(cond_subst, range_cond)
+cond_subst = tir.ir_pass.Simplify(cond_subst)
+check_bruteforce(te.all(cond_subst, cond_on_vars), all_vranges,
+ cond=te.all(tir.const(1, 'bool'),
*formula1.relations))
+
+rels = solution.dst.relations
+if len(rels) == 1 and ir.structural_equal(rels[0], False):
+# not solvable, skip
+return
+_check_forward(solution.src, solution.dst,
+ solution.src_to_dst, solution.dst_to_src)
+_check_forward(solution.dst, solution.src,
+ solution.dst_to_src, solution.src_to_dst)
+
+
+def test_solution_consistency():
+random.seed(0)
Review comment:
Personally, I think it is a good idea. I'm just worried that people won't
know what to do with such failure and will just complain about flaky tests
instead of reporting the unlucky seed. Maybe print some message like "this test
is intentionally nondeterministic, if it fails please report it together with
this seed"?
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[GitHub] [incubator-tvm] masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610695078 @zhiics Yes, I'll review and merge this today @alexbooth The error you get is because now with composite, op inside CallNode can be a FunctionNode, rather than OpNode. I'll send a DNNL change today so 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 With regards, Apache Git Services
[GitHub] [incubator-tvm] zhiics commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
zhiics commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610690039 @masahi yeah, inlining the function you pointed out is a little tricky because it varies case by case. I intentionally made it a bit more conservative and left it for the external codegen to handle. In the long run, it would be more helpful if we make the passes more configurable. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] yzhliu commented on a change in pull request #5171: [Arith] linear system and equation solver
yzhliu commented on a change in pull request #5171: [Arith] linear system and
equation solver
URL: https://github.com/apache/incubator-tvm/pull/5171#discussion_r405194241
##
File path: tests/python/unittest/test_arith_solve_linear_system.py
##
@@ -14,9 +14,130 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
+import random
+import numpy as np
import tvm
-from tvm import te, arith
-from tvm.tir import ir_pass
+from tvm import te, arith, ir, tir
+
+
+def run_expr(expr, vranges):
+def _compute_body(*us):
+vmap = {v: u + r.min for (v, r), u in zip(vranges.items(), us)}
+return tir.ir_pass.Substitute(expr, vmap)
+
+A = te.compute([r.extent.value for v, r in vranges.items()], _compute_body)
+args = [tvm.nd.empty(A.shape, A.dtype)]
+sch = te.create_schedule(A.op)
+mod = tvm.build(sch, [A])
+mod(*args)
+return args[0].asnumpy()
+
+
+def check_bruteforce(bool_expr, vranges, cond=None):
+if cond is not None:
+bool_expr = te.any(tir.Not(cond), bool_expr)
+
+res = run_expr(bool_expr, vranges)
+if not np.all(res):
+indices = list(np.argwhere(res == 0)[0])
+counterex = [(str(v), i + r.min) for (v, r), i in zip(vranges.items(),
indices)]
+counterex = sorted(counterex, key=lambda x: x[0])
+counterex = ", ".join([v + " = " + str(i) for v, i in counterex])
+raise AssertionError("Expression {}\nis not true on {}\n"
+ "Counterexample: {}"
+ .format(tir.ir_pass.CanonicalSimplify(bool_expr),
vranges, counterex))
+
+
+def check_solution(solution, vranges={}):
+def _check_forward(formula1, formula2, varmap, backvarmap):
+all_vranges = vranges.copy()
+all_vranges.update({v: r for v, r in formula1.ranges.items()})
+
+# Check that the transformation is injective
+cond_on_vars = tir.const(1, 'bool')
+for v in formula1.variables:
+# variable mapping is consistent
+v_back = tir.ir_pass.Simplify(tir.ir_pass.Substitute(varmap[v],
backvarmap))
+cond_on_vars = te.all(cond_on_vars, v == v_back)
+# Also we have to check that the new relations are true when old
relations are true
+cond_subst = tir.ir_pass.Substitute(
+te.all(tir.const(1, 'bool'), *formula2.relations), backvarmap)
+# We have to include relations from vranges too
+for v in formula2.variables:
+if v in formula2.ranges:
+r = formula2.ranges[v]
+range_cond = te.all(v >= r.min, v < r.min + r.extent)
+range_cond = tir.ir_pass.Substitute(range_cond, backvarmap)
+cond_subst = te.all(cond_subst, range_cond)
+cond_subst = tir.ir_pass.Simplify(cond_subst)
+check_bruteforce(te.all(cond_subst, cond_on_vars), all_vranges,
+ cond=te.all(tir.const(1, 'bool'),
*formula1.relations))
+
+rels = solution.dst.relations
+if len(rels) == 1 and ir.structural_equal(rels[0], False):
+# not solvable, skip
+return
+_check_forward(solution.src, solution.dst,
+ solution.src_to_dst, solution.dst_to_src)
+_check_forward(solution.dst, solution.src,
+ solution.dst_to_src, solution.src_to_dst)
+
+
+def test_solution_consistency():
+random.seed(0)
Review comment:
how about we always use random seed, and print out the seed so that we will
be able to reproduce once triggered?
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[GitHub] [incubator-tvm] zhiics commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
zhiics commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610688475 @alexbooth Thanks for pointing out. The purpose of the codegen_c is mainly for quick prototyping. Would you be interested to send a PR to add the support for composite function after this is merged? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] zhiics commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
zhiics commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610687622 @masahi @trevor-m can you guys help review as well? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
anijain2305 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610685005 > @anijain2305 What do you mean ARM CPUs? mxnet doesn't have pip wheel for ARM. Nvm. I got confused. I was thinking that if we add ARM CPUs to CI in future, will it cause issues (as mxnet-mkl is not supported on ARM). This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] anijain2305 edited a comment on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
anijain2305 edited a comment on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610685005 > @anijain2305 What do you mean ARM CPUs? mxnet doesn't have pip wheel for ARM. Nvm. I got confused. I was thinking that if we add ARM CPUs to CI in future, will it cause issues (as mxnet-mkl is not supported on ARM)? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] shoubhik commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
shoubhik commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610684437 If mxnet-mkl does not break any existing functionality we should probably take it as a dependency. That gives us enough wiggle room and infra in the future to experiment on how we can add tests for mxnet Qnn. What do you guys think? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
icemelon9 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610683044 @anijain2305 What do you mean ARM CPUs? mxnet doesn't have pip wheel for ARM. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610682499 Ok got the DNNL conv + bias + relu working (https://github.com/masahi/tvm/compare/byoc-composite...masahi:dnnl-composite). This is a reimplmentation of https://github.com/apache/incubator-tvm/pull/4741 based on composite annotate support in this PR, rather than the manual approach. I think it is much cleaner. I can send a new PR as soon as this PR is merged. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #4459: [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM runtime
tqchen commented on issue #4459: [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM runtime URL: https://github.com/apache/incubator-tvm/pull/4459#issuecomment-610677853 Thanks @tobegit3hub @FrozenGene @zhiics @soiferj @wrongtest @gmagogsfm @yzhliu . This PR is now merged This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (4e00763 -> 53a4ad3)
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 4e00763 [LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+
(#5267)
add 53a4ad3 [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM
runtime (#4459)
No new revisions were added by this update.
Summary of changes:
CMakeLists.txt | 2 +
.../tf_tvmdsoop/CMakeLists.txt | 27 +-
.../tf_tvmdsoop/prepare_and_test_tfop_module.sh| 21 +-
apps/tf_tvmdsoop/tests/test_tfop_module.py | 118
cmake/config.cmake | 4 +
cmake/modules/contrib/TF_TVMDSOOP.cmake| 58
.../device/arm => contrib/tf_op}/__init__.py | 5 +-
python/tvm/contrib/tf_op/module.py | 113
src/contrib/tf_op/tvm_dso_op_kernels.cc| 310 +
.../contrib/tf_op/tvm_dso_ops.cc | 40 +--
tests/scripts/task_python_integration.sh | 3 +
11 files changed, 649 insertions(+), 52 deletions(-)
copy docker/Dockerfile.ci_lint => apps/tf_tvmdsoop/CMakeLists.txt (58%)
copy tests/scripts/task_python_nightly.sh =>
apps/tf_tvmdsoop/prepare_and_test_tfop_module.sh (60%)
mode change 100755 => 100644
create mode 100644 apps/tf_tvmdsoop/tests/test_tfop_module.py
create mode 100644 cmake/modules/contrib/TF_TVMDSOOP.cmake
copy python/tvm/{micro/device/arm => contrib/tf_op}/__init__.py (89%)
create mode 100644 python/tvm/contrib/tf_op/module.py
create mode 100644 src/contrib/tf_op/tvm_dso_op_kernels.cc
copy apps/ios_rpc/tvmrpcLauncher/tvmrpcLauncher.mm =>
src/contrib/tf_op/tvm_dso_ops.cc (63%)
[GitHub] [incubator-tvm] tqchen merged pull request #4459: [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM runtime
tqchen merged pull request #4459: [RUNTIME] Implement TVMDSOOp(TensorFlow custom op) for TVM runtime URL: https://github.com/apache/incubator-tvm/pull/4459 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen merged pull request #5267: [LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+
tqchen merged pull request #5267: [LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+ URL: https://github.com/apache/incubator-tvm/pull/5267 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (2942278 -> 4e00763)
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 2942278 [RUNTIME] Quick fix PackedFunc String passing (#5266) add 4e00763 [LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+ (#5267) No new revisions were added by this update. Summary of changes: src/target/llvm/codegen_x86_64.cc | 6 +- 1 file changed, 5 insertions(+), 1 deletion(-)
[GitHub] [incubator-tvm] anijain2305 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
anijain2305 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610675721 How about ARM CPUs? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
icemelon9 commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610673485 According to the link @leezu shared, mxnet-mkl is able to run on AMD CPU. So should we just keep mxnet-mkl so that @shoubhik can use the quantization feature for test cases? @tqchen This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 edited a comment on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
icemelon9 edited a comment on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610673485 Thanks @leezu for the clarification. According to the link @leezu shares, mxnet-mkl is able to run on AMD CPU. So should we just keep mxnet-mkl so that @shoubhik can use the quantization feature for test cases? @tqchen This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (df8a6f3 -> 2942278)
This is an automated email from the ASF dual-hosted git repository. zhic pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from df8a6f3 [LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors (#5265) add 2942278 [RUNTIME] Quick fix PackedFunc String passing (#5266) No new revisions were added by this update. Summary of changes: include/tvm/runtime/packed_func.h | 14 ++ tests/cpp/packed_func_test.cc | 6 ++ 2 files changed, 16 insertions(+), 4 deletions(-)
[GitHub] [incubator-tvm] zhiics merged pull request #5266: [RUNTIME] Quick fix PackedFunc String passing
zhiics merged pull request #5266: [RUNTIME] Quick fix PackedFunc String passing URL: https://github.com/apache/incubator-tvm/pull/5266 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610666034 @trevor-m Yeah agree. I'm looking to support either function call or "manual inline" in the codegen. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] leezu commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
leezu commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610664896 Any release after mxnet 1.6 will build with mkldnn by default. If you don't want to use mkldnn, you need to use mxnet-native package in the future. See https://lists.apache.org/thread.html/1a22dbd79098adab6d02d16e8d607bae2acc908c0bb1b085d28a51ba@%3Cdev.mxnet.apache.org%3E This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] leezu commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version
leezu commented on issue #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270#issuecomment-610664463 mxnet-mkl is not build with mkl but only mkldnn. The name is confusing. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] trevor-m commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
trevor-m commented on issue #5261: [RELAY][BYOC] Add support for composite
functions in BYOC
URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610663673
> Using MergeComposite, AnnotateTarget and PartitionGraph, I get the
following graph for conv + bias + relu pattern:
>
> ```
> def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x55a8d9cddbd0), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
> %2 = fn (%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(1,
3, 3, 3), float32], %bias: Tensor[(1, 1, 1), float32],
Composite="dnnl.conv_bias_relu") -> Tensor[(1, 1, 224, 224), float32] {
> %0 = nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=1,
kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
> %1 = add(%0, %bias) /* ty=Tensor[(1, 1, 224, 224), float32] */;
> nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
> };
> %2(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3, 3),
float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, meta[relay.Constant][1] /*
ty=Tensor[(1, 1, 1), float32] */ /* ty=Tensor[(1, 1, 1), float32] */) /*
ty=Tensor[(1, 1, 224, 224), float32] */
> }
>
> def @main(%data1: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
> @dnnl_0(%data1) /* ty=Tensor[(1, 1, 224, 224), float32] */
> }
> ```
>
> Is it possible to inline composite function `%2` there into `dnnl_0`? What
I want is this:
>
> ```
> def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x5599b307c370), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
> %0 = nn.conv2d(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3,
3, 3), float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, padding=[1, 1, 1,
1], channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
> %1 = add(%0, meta[relay.Constant][1] /* ty=Tensor[(1, 1, 1), float32] */
/* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(1, 1, 224, 224), float32] */;
> nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
> }
>
> def @main(%data: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
> @dnnl_0(%data) /* ty=Tensor[(1, 1, 224, 224), float32] */
> }
> ```
>
> Otherwise I have to support function call in DNNL codegen. @zhiics @mbaret
>
> UPDATE: hmm if I inline the composite function, I lose the composite
attribute and hence cannot detect fused call. Is supporting function call in
the DNNL codegen a better option?
Hi @masahi, I think it is best to leave this to the codegen. That way we
have the option to handle a composite all at once at call node or invididually
by just doing VisitExpr(func->body) when we encounter one.
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[GitHub] [incubator-tvm] masahi edited a comment on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi edited a comment on issue #5261: [RELAY][BYOC] Add support for composite
functions in BYOC
URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610659753
Using MergeComposite, AnnotateTarget and PartitionGraph, I get the following
graph for conv + bias + relu pattern:
```
def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x55a8d9cddbd0), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
%2 = fn (%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(1, 3,
3, 3), float32], %bias: Tensor[(1, 1, 1), float32],
Composite="dnnl.conv_bias_relu") -> Tensor[(1, 1, 224, 224), float32] {
%0 = nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=1,
kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
%1 = add(%0, %bias) /* ty=Tensor[(1, 1, 224, 224), float32] */;
nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
};
%2(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3, 3), float32]
*/ /* ty=Tensor[(1, 3, 3, 3), float32] */, meta[relay.Constant][1] /*
ty=Tensor[(1, 1, 1), float32] */ /* ty=Tensor[(1, 1, 1), float32] */) /*
ty=Tensor[(1, 1, 224, 224), float32] */
}
def @main(%data1: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
@dnnl_0(%data1) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
```
Is it possible to inline composite function `%2` there into `dnnl_0`? What I
want is this:
```
def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x5599b307c370), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
%0 = nn.conv2d(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3,
3), float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, padding=[1, 1, 1, 1],
channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
%1 = add(%0, meta[relay.Constant][1] /* ty=Tensor[(1, 1, 1), float32] */
/* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(1, 1, 224, 224), float32] */;
nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
def @main(%data: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
@dnnl_0(%data) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
```
Otherwise I have to support function call in DNNL codegen. @zhiics @mbaret
UPDATE: hmm if I inline the composite function, I lose the composite
attribute and hence cannot detect fused call. Is supporting function call in
the DNNL codegen a better option?
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[GitHub] [incubator-tvm] masahi edited a comment on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi edited a comment on issue #5261: [RELAY][BYOC] Add support for composite
functions in BYOC
URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610659753
Using MergeComposite, AnnotateTarget and PartitionGraph, I get the following
graph for conv + bias + relu pattern:
```
def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x55a8d9cddbd0), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
%2 = fn (%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(1, 3,
3, 3), float32], %bias: Tensor[(1, 1, 1), float32],
Composite="dnnl.conv_bias_relu") -> Tensor[(1, 1, 224, 224), float32] {
%0 = nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=1,
kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
%1 = add(%0, %bias) /* ty=Tensor[(1, 1, 224, 224), float32] */;
nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
};
%2(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3, 3), float32]
*/ /* ty=Tensor[(1, 3, 3, 3), float32] */, meta[relay.Constant][1] /*
ty=Tensor[(1, 1, 1), float32] */ /* ty=Tensor[(1, 1, 1), float32] */) /*
ty=Tensor[(1, 1, 224, 224), float32] */
}
def @main(%data1: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
@dnnl_0(%data1) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
```
Is it possible to inline composite function `%2` there into `dnnl_0`? What I
want is this:
```
def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x5599b307c370), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
%0 = nn.conv2d(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3,
3), float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, padding=[1, 1, 1, 1],
channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
%1 = add(%0, meta[relay.Constant][1] /* ty=Tensor[(1, 1, 1), float32] */
/* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(1, 1, 224, 224), float32] */;
nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
def @main(%data: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
@dnnl_0(%data) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
```
Otherwise I have to support function call in DNNL codegen. @zhiics @mbaret
UPDATE: hmm if I inline the composite function, I lose the composite
attribute and hence cannot detect fused call. Is supporting functional call in
DNNL a better option?
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[GitHub] [incubator-tvm] masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi commented on issue #5261: [RELAY][BYOC] Add support for composite
functions in BYOC
URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610659753
Using MergeComposite, AnnotateTarget and PartitionGraph, I get the following
graph for conv + bias + relu pattern:
```
def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x55a8d9cddbd0), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
%2 = fn (%data: Tensor[(1, 3, 224, 224), float32], %weight: Tensor[(1, 3,
3, 3), float32], %bias: Tensor[(1, 1, 1), float32],
Composite="dnnl.conv_bias_relu") -> Tensor[(1, 1, 224, 224), float32] {
%0 = nn.conv2d(%data, %weight, padding=[1, 1, 1, 1], channels=1,
kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
%1 = add(%0, %bias) /* ty=Tensor[(1, 1, 224, 224), float32] */;
nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
};
%2(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3, 3), float32]
*/ /* ty=Tensor[(1, 3, 3, 3), float32] */, meta[relay.Constant][1] /*
ty=Tensor[(1, 1, 1), float32] */ /* ty=Tensor[(1, 1, 1), float32] */) /*
ty=Tensor[(1, 1, 224, 224), float32] */
}
def @main(%data1: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
@dnnl_0(%data1) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
```
Is it possible to inline composite function `%2` there into `dnnl_0`? What I
want is this:
```
def @dnnl_0(%dnnl_0_i0: Tensor[(1, 3, 224, 224), float32], Inline=1,
Compiler="dnnl", global_symbol=runtime.String(0x5599b307c370), Primitive=1) ->
Tensor[(1, 1, 224, 224), float32] {
%0 = nn.conv2d(%dnnl_0_i0, meta[relay.Constant][0] /* ty=Tensor[(1, 3, 3,
3), float32] */ /* ty=Tensor[(1, 3, 3, 3), float32] */, padding=[1, 1, 1, 1],
channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 224, 224), float32] */;
%1 = add(%0, meta[relay.Constant][1] /* ty=Tensor[(1, 1, 1), float32] */
/* ty=Tensor[(1, 1, 1), float32] */) /* ty=Tensor[(1, 1, 224, 224), float32] */;
nn.relu(%1) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
def @main(%data: Tensor[(1, 3, 224, 224), float32]) -> Tensor[(1, 1, 224,
224), float32] {
@dnnl_0(%data) /* ty=Tensor[(1, 1, 224, 224), float32] */
}
```
Otherwise I have to support function call in DNNL codegen. @zhiics @mbaret
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[incubator-tvm] branch master updated: [LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors (#5265)
This is an automated email from the ASF dual-hosted git repository.
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 df8a6f3 [LLVM] Use llvm::ElementCount with LLVM 11+ when creating
vectors (#5265)
df8a6f3 is described below
commit df8a6f3b85b2ae24560d6cac7587ce6a6f08033c
Author: Krzysztof Parzyszek
AuthorDate: Tue Apr 7 17:49:07 2020 -0500
[LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors (#5265)
LLVM 11 added support for scalable vectors, and now the number of
elements in a vector is represented by a llvm::ElementCount class,
not just a number.
---
src/target/llvm/codegen_llvm.cc | 5 +
1 file changed, 5 insertions(+)
diff --git a/src/target/llvm/codegen_llvm.cc b/src/target/llvm/codegen_llvm.cc
index bd2cd9f..28f4efd 100644
--- a/src/target/llvm/codegen_llvm.cc
+++ b/src/target/llvm/codegen_llvm.cc
@@ -463,7 +463,12 @@ llvm::Value* CodeGenLLVM::CreateBroadcast(llvm::Value*
value, int lanes) {
llvm::VectorType::get(value->getType(), lanes));
llvm::Constant* zero = ConstInt32(0);
value = builder_->CreateInsertElement(undef, value, zero);
+#if TVM_LLVM_VERSION >= 110
+ llvm::Constant* mask =
+ llvm::ConstantVector::getSplat(llvm::ElementCount(lanes,
/*Scalable=*/false), zero);
+#else
llvm::Constant* mask = llvm::ConstantVector::getSplat(lanes, zero);
+#endif
return builder_->CreateShuffleVector(value, undef, mask);
}
[GitHub] [incubator-tvm] tqchen merged pull request #5265: [LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors
tqchen merged pull request #5265: [LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors URL: https://github.com/apache/incubator-tvm/pull/5265 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (e11a609 -> 36ce2e2)
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 e11a609 [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT (#5124) add 36ce2e2 [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings (#5264) No new revisions were added by this update. Summary of changes: src/target/llvm/codegen_cpu.cc | 32 src/target/llvm/codegen_llvm.cc | 31 +++ 2 files changed, 59 insertions(+), 4 deletions(-)
[GitHub] [incubator-tvm] tqchen merged pull request #5264: [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings
tqchen merged pull request #5264: [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings URL: https://github.com/apache/incubator-tvm/pull/5264 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tmoreau89 commented on issue #5124: [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT
tmoreau89 commented on issue #5124: [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT URL: https://github.com/apache/incubator-tvm/pull/5124#issuecomment-610632004 Thanks @liangfu @tqchen the PR has been merged. For the CRT CI tests, I suggest these get added in a follow up PR. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated: [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT (#5124)
This is an automated email from the ASF dual-hosted git repository.
moreau 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 e11a609 [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT
(#5124)
e11a609 is described below
commit e11a6092e629cadd34af1f48be47817ca9c65fd4
Author: Liangfu Chen
AuthorDate: Wed Apr 8 05:33:05 2020 +0800
[uTVM][Runtime] Introduce Virtual Memory Allocator to CRT (#5124)
* initial crt_memory and memory leak fix in graph_runtime
Change-Id: I0f79f909a04d1c677aabb80f202f0612c5ce7f2a
* fix memory leak
Change-Id: I37104c09e28112b1974fa2b064c809d0a8d686c3
* clean up
Change-Id: I039b12015a1d56c8f4120867cd5a5292da34f3e3
* implement vrealloc
Change-Id: I35800470bcbfcf96652494f359711cb4c2d34398
* allocate from stack memory for most of the variables
Change-Id: I72071289843fff4031c0df8796868a0b9fbc57ee
* allocate from stack memory for all of the variables
Change-Id: I32dba85ac1660c77f51c2d0d8ab6436ed0c01c74
* lint
Change-Id: If12cd240685d7791fc60bc0cfb66389cdc186b73
* lint
Change-Id: I7c9d90c11b60b8edda2427ebd189ebe535af2100
* facilitate the growth of TVM_CRT_MAX_NDIM
Change-Id: I939fa43027a5c7529c5c7c6bd8d6e6beb91b7581
* extend test coverage of vmalloc
Change-Id: Ie4ff6b64fdfe6810836cf8fd44dace82a20c4581
* lint
Change-Id: Ibf3c06619ef296df5c49f3945cb642881d69
* move logging.h to src
* fix an error in macOS
* remove logging.h
* use cflags for gcc
* fix compilation error
---
apps/bundle_deploy/Makefile| 16 +-
apps/bundle_deploy/demo.cc | 10 +-
apps/bundle_deploy/runtime.c | 40 ++-
apps/bundle_deploy/test.cc | 10 +-
.../module.h => include/tvm/runtime/crt/memory.h | 46 +--
src/runtime/crt/crt_backend_api.c | 12 +-
src/runtime/crt/graph_runtime.c| 202 +++
src/runtime/crt/graph_runtime.h| 43 +--
src/runtime/crt/load_json.c| 24 +-
src/runtime/crt/logging.h | 73
src/runtime/crt/memory.c | 393 +
src/runtime/crt/module.h | 5 +-
src/runtime/crt/ndarray.c | 10 +-
src/runtime/crt/packed_func.h | 10 +-
.../crt/module.h => tests/cpp/crt_memory_test.cc | 55 +--
15 files changed, 757 insertions(+), 192 deletions(-)
diff --git a/apps/bundle_deploy/Makefile b/apps/bundle_deploy/Makefile
index c80765f..73f9d75 100644
--- a/apps/bundle_deploy/Makefile
+++ b/apps/bundle_deploy/Makefile
@@ -20,11 +20,11 @@
# Setup build environment
TVM_ROOT=$(shell cd ../..; pwd)
DMLC_CORE=${TVM_ROOT}/3rdparty/dmlc-core
-PKG_CXXFLAGS = -std=c++14 -O2 -fPIC \
+PKG_CXXFLAGS = -Wall -std=c++14 -O2 -fPIC \
-I${TVM_ROOT}/include \
-I${DMLC_CORE}/include \
-I${TVM_ROOT}/3rdparty/dlpack/include
-PKG_CFLAGS = -std=c99 -O2 -fPIC \
+PKG_CFLAGS = -Wall -std=c99 -O2 -fPIC \
-I${TVM_ROOT}/include \
-I${DMLC_CORE}/include \
-I${TVM_ROOT}/3rdparty/dlpack/include
@@ -57,11 +57,11 @@ $(build_dir)/test_dynamic: test.cc
${build_dir}/test_graph.json ${build_dir}/tes
$(build_dir)/demo_static: demo_static.c ${build_dir}/bundle_static.o
${build_dir}/model.o ${build_dir}/graph.json.c ${build_dir}/params.bin.c
@mkdir -p $(@D)
- gcc $(PKG_CXXFLAGS) -o $@ demo_static.c ${build_dir}/bundle_static.o
${build_dir}/model.o -lm
+ gcc $(PKG_CFLAGS) -o $@ demo_static.c ${build_dir}/bundle_static.o
${build_dir}/model.o -lm
$(build_dir)/test_static: test_static.c ${build_dir}/bundle_static.o
${build_dir}/test_model.o
@mkdir -p $(@D)
- gcc $(PKG_CXXFLAGS) -o $@ $^
+ gcc $(PKG_CFLAGS) -o $@ $^
# Serialize our graph.json file.
$(build_dir)/graph.json.c: $(build_dir)/graph.json
@@ -71,14 +71,6 @@ $(build_dir)/graph.json.c: $(build_dir)/graph.json
$(build_dir)/params.bin.c: $(build_dir)/params.bin
xxd -i $^ > $@
-# # Serialize our test_graph.json file.
-# $(build_dir)/test_graph.json.c: $(build_dir)/test_graph.json
-# xxd -i $^ > $@
-#
-# # Serialize our test_params.bin file.
-# $(build_dir)/test_params.bin.c: $(build_dir)/test_params.bin
-# xxd -i $^ > $@
-
$(build_dir)/model.o $(build_dir)/graph.json $(build_dir)/params.bin
$(build_dir)/cat.bin: build_model.py
python3 $< -o $(build_dir)
diff --git a/apps/bundle_deploy/demo.cc b/apps/bundle_deploy/demo.cc
index 34be279..0de10d7 100644
--- a/apps/bundle_deploy/demo.cc
+++ b/apps/bundle_deploy/demo.cc
@@ -109,11 +109,11 @@ int main(int
[GitHub] [incubator-tvm] tmoreau89 merged pull request #5124: [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT
tmoreau89 merged pull request #5124: [uTVM][Runtime] Introduce Virtual Memory Allocator to CRT URL: https://github.com/apache/incubator-tvm/pull/5124 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 opened a new pull request #5270: [CI] Change MxNet from MKL verion to regular CPU version
icemelon9 opened a new pull request #5270: [CI] Change MxNet from MKL verion to regular CPU version URL: https://github.com/apache/incubator-tvm/pull/5270 Because mxnet-mkl doesn't support AMD cpu. see #5240 cc @tqchen @anijain2305 @shoubhik This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 opened a new pull request #5269: [LINT] Remove scalalint from lint deps
icemelon9 opened a new pull request #5269: [LINT] Remove scalalint from lint deps URL: https://github.com/apache/incubator-tvm/pull/5269 Thanks for contributing to TVM! Please refer to guideline https://tvm.apache.org/docs/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread. cc @tqchen This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kparzysz-quic opened a new pull request #5268: [LLVM] Include Support/Host.h for declaration of getDefaultTargetTriple
kparzysz-quic opened a new pull request #5268: [LLVM] Include Support/Host.h for declaration of getDefaultTargetTriple URL: https://github.com/apache/incubator-tvm/pull/5268 In newer versions of LLVM, this header is no longer included by one of the already included headers in `llvm_common.h`, so include it explicitly. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kparzysz-quic opened a new pull request #5267: [LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+
kparzysz-quic opened a new pull request #5267: [LLVM] Do not use x86_vcvtph2ps_256 intrinsic with LLVM 11+ URL: https://github.com/apache/incubator-tvm/pull/5267 This intrinsic was removed in LLVM 11. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #5264: [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings
tqchen commented on issue #5264: [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings URL: https://github.com/apache/incubator-tvm/pull/5264#issuecomment-610607243 Thanks @kparzysz-quic This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on a change in pull request #5251: [RUNTIME] Auto conversion from str to runtime::String in PackedFUnc
tqchen commented on a change in pull request #5251: [RUNTIME] Auto conversion
from str to runtime::String in PackedFUnc
URL: https://github.com/apache/incubator-tvm/pull/5251#discussion_r405095536
##
File path: include/tvm/runtime/packed_func.h
##
@@ -554,6 +512,10 @@ class TVMArgValue : public TVMPODValue_ {
return std::string(value_.v_str);
}
}
+ operator tvm::runtime::String() const {
+// directly use the std::string constructor for now.
+return tvm::runtime::String(operator std::string());
Review comment:
https://github.com/apache/incubator-tvm/pull/5266
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[GitHub] [incubator-tvm] tqchen opened a new pull request #5266: [RUNTIME] Quick fix PackedFunc String passing
tqchen opened a new pull request #5266: [RUNTIME] Quick fix PackedFunc String passing URL: https://github.com/apache/incubator-tvm/pull/5266 cc @zhiics This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #5251: [RUNTIME] Auto conversion from str to runtime::String in PackedFUnc
zhiics commented on a change in pull request #5251: [RUNTIME] Auto conversion
from str to runtime::String in PackedFUnc
URL: https://github.com/apache/incubator-tvm/pull/5251#discussion_r405092183
##
File path: include/tvm/runtime/packed_func.h
##
@@ -554,6 +512,10 @@ class TVMArgValue : public TVMPODValue_ {
return std::string(value_.v_str);
}
}
+ operator tvm::runtime::String() const {
+// directly use the std::string constructor for now.
+return tvm::runtime::String(operator std::string());
Review comment:
so let's just `return AsObjectRef()` for now?
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[GitHub] [incubator-tvm] kparzysz-quic opened a new pull request #5265: [LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors
kparzysz-quic opened a new pull request #5265: [LLVM] Use llvm::ElementCount with LLVM 11+ when creating vectors URL: https://github.com/apache/incubator-tvm/pull/5265 LLVM 11 added support for scalable vectors, and now the number of elements in a vector is represented by a `llvm::ElementCount` class, not just a number. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kparzysz-quic opened a new pull request #5264: [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings
kparzysz-quic opened a new pull request #5264: [LLVM] Use llvm::Align with LLVM 11+ to avoid warnings URL: https://github.com/apache/incubator-tvm/pull/5264 LLVM 11 is introducing a separate class to represent alignment. The functions in IRBuilder that create aligned loads and stores, and which accept the alignment as an unsigned value have been deprecated (and now cause warnings to be emitted). This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on a change in pull request #5251: [RUNTIME] Auto conversion from str to runtime::String in PackedFUnc
tqchen commented on a change in pull request #5251: [RUNTIME] Auto conversion
from str to runtime::String in PackedFUnc
URL: https://github.com/apache/incubator-tvm/pull/5251#discussion_r405065312
##
File path: include/tvm/runtime/packed_func.h
##
@@ -554,6 +512,10 @@ class TVMArgValue : public TVMPODValue_ {
return std::string(value_.v_str);
}
}
+ operator tvm::runtime::String() const {
+// directly use the std::string constructor for now.
+return tvm::runtime::String(operator std::string());
Review comment:
Ah, i see, good catch, we will need to add a patch, by checking if the
result is kStr and run this, alternatively, use AsObjectRef
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[GitHub] [incubator-tvm] tqchen commented on a change in pull request #5251: [RUNTIME] Auto conversion from str to runtime::String in PackedFUnc
tqchen commented on a change in pull request #5251: [RUNTIME] Auto conversion
from str to runtime::String in PackedFUnc
URL: https://github.com/apache/incubator-tvm/pull/5251#discussion_r405065312
##
File path: include/tvm/runtime/packed_func.h
##
@@ -554,6 +512,10 @@ class TVMArgValue : public TVMPODValue_ {
return std::string(value_.v_str);
}
}
+ operator tvm::runtime::String() const {
+// directly use the std::string constructor for now.
+return tvm::runtime::String(operator std::string());
Review comment:
Ah, i see, we will need to add a patch, by checking if the result is kStr
and run this, alternatively, use AsObjectRef
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[GitHub] [incubator-tvm] zhiics commented on issue #5250: [REFACTOR] StringImm -> String
zhiics commented on issue #5250: [REFACTOR] StringImm -> String URL: https://github.com/apache/incubator-tvm/issues/5250#issuecomment-610567541 @tqchen I will spend some time on this. I just had simple test based on #5251. It looks I had a problem. Could you please 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 With regards, Apache Git Services
[GitHub] [incubator-tvm] zhiics edited a comment on issue #5250: [REFACTOR] StringImm -> String
zhiics edited a comment on issue #5250: [REFACTOR] StringImm -> String URL: https://github.com/apache/incubator-tvm/issues/5250#issuecomment-610567541 @tqchen I will spend some time on this. I just had simple test based on #5251. It looks I had a problem. Could you please take a look at the comment there? This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] zhiics commented on a change in pull request #5251: [RUNTIME] Auto conversion from str to runtime::String in PackedFUnc
zhiics commented on a change in pull request #5251: [RUNTIME] Auto conversion
from str to runtime::String in PackedFUnc
URL: https://github.com/apache/incubator-tvm/pull/5251#discussion_r405046250
##
File path: include/tvm/runtime/packed_func.h
##
@@ -554,6 +512,10 @@ class TVMArgValue : public TVMPODValue_ {
return std::string(value_.v_str);
}
}
+ operator tvm::runtime::String() const {
+// directly use the std::string constructor for now.
+return tvm::runtime::String(operator std::string());
Review comment:
@tqchen It happened to me that Line511 above failed for the check because
the type_code_ for String is an object. Should we remove this and pass String
objectref directly? Or do we need to handle String through FFI?
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[GitHub] [incubator-tvm] anijain2305 commented on issue #5241: [Relay][OP] Add fast_erf implementation
anijain2305 commented on issue #5241: [Relay][OP] Add fast_erf implementation URL: https://github.com/apache/incubator-tvm/pull/5241#issuecomment-610566829 Thanks @icemelon9 @tqchen This is merged This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated: [Relay][OP] Add fast_erf implementation (#5241)
This is an automated email from the ASF dual-hosted git repository.
anijain2305 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 f5b02fd [Relay][OP] Add fast_erf implementation (#5241)
f5b02fd is described below
commit f5b02fdb1b5a7b6be79df97035ec1c3b80e3c665
Author: Haichen Shen
AuthorDate: Tue Apr 7 12:05:33 2020 -0700
[Relay][OP] Add fast_erf implementation (#5241)
* add fast erf
* doc
* lint
* fix
* fix indent
---
include/tvm/target/generic_func.h | 2 +-
python/tvm/relay/op/_tensor.py | 2 +
src/relay/op/tensor/unary.cc| 11 +
src/relay/transforms/fast_math.cc | 4 ++
src/relay/transforms/pattern_util.h | 5 +++
tests/python/relay/test_op_fast_math.py | 3 ++
topi/include/topi/elemwise.h| 73 -
topi/python/topi/math.py| 16
topi/src/elemwise.cc| 5 +++
topi/tests/python/test_topi_math.py | 9 ++--
10 files changed, 124 insertions(+), 6 deletions(-)
diff --git a/include/tvm/target/generic_func.h
b/include/tvm/target/generic_func.h
index 89a7f57..f2a361b3 100644
--- a/include/tvm/target/generic_func.h
+++ b/include/tvm/target/generic_func.h
@@ -72,7 +72,7 @@ class GenericFunc : public ObjectRef {
*
* \code
* // Example code on how to call generic function
- * void CallGeneirc(GenericFunc f) {
+ * void CallGeneric(GenericFunc f) {
* // call like normal functions by pass in arguments
* // return value is automatically converted back
* int rvalue = f(1, 2.0);
diff --git a/python/tvm/relay/op/_tensor.py b/python/tvm/relay/op/_tensor.py
index f24da05..a607a47 100644
--- a/python/tvm/relay/op/_tensor.py
+++ b/python/tvm/relay/op/_tensor.py
@@ -76,6 +76,7 @@ register_injective_schedule("shape_of")
register_injective_schedule("ndarray_size")
register_broadcast_schedule("fast_exp")
register_broadcast_schedule("fast_tanh")
+register_broadcast_schedule("fast_erf")
# zeros
@@ -222,3 +223,4 @@ register_shape_func("exp", False, elemwise_shape_func)
register_shape_func("tan", False, elemwise_shape_func)
register_shape_func("fast_exp", False, elemwise_shape_func)
register_shape_func("fast_tanh", False, elemwise_shape_func)
+register_shape_func("fast_erf", False, elemwise_shape_func)
diff --git a/src/relay/op/tensor/unary.cc b/src/relay/op/tensor/unary.cc
index 3da77e9..4cca8b0 100644
--- a/src/relay/op/tensor/unary.cc
+++ b/src/relay/op/tensor/unary.cc
@@ -128,6 +128,17 @@ RELAY_REGISTER_UNARY_OP("erf")
.set_attr("FTVMCompute", RELAY_UNARY_COMPUTE(topi::erf));
+RELAY_REGISTER_UNARY_OP("fast_erf")
+.describe(R"code(Returns the error function value for input array, computed
element-wise.
+
+.. math::
+ \fast_erf(x)
+
+)code" TVM_ADD_FILELINE)
+.set_support_level(1)
+.set_attr("FTVMCompute", RELAY_UNARY_COMPUTE(topi::fast_erf));
+
+
RELAY_REGISTER_UNARY_OP("sqrt")
.describe(R"code(Returns the sqrt input array, computed element-wise.
diff --git a/src/relay/transforms/fast_math.cc
b/src/relay/transforms/fast_math.cc
index 861566f..cf00a89 100644
--- a/src/relay/transforms/fast_math.cc
+++ b/src/relay/transforms/fast_math.cc
@@ -35,11 +35,14 @@ class FastMathMutator : public ExprRewriter {
public:
FastMathMutator()
: exp_op_(Op::Get("exp")),
+erf_op_(Op::Get("erf")),
tanh_op_(Op::Get("tanh")) {}
Expr Rewrite_(const CallNode* pre, const Expr& post) override {
if (pre->op == exp_op_) {
return FastExp(post.as()->args[0]);
+} else if (pre->op == erf_op_) {
+ return FastErf(post.as()->args[0]);
} else if (pre->op == tanh_op_) {
return FastTanh(post.as()->args[0]);
}
@@ -51,6 +54,7 @@ class FastMathMutator : public ExprRewriter {
// operator equivalence checking so that the registry lookup overhead can be
// reduced.
const Op& exp_op_;
+ const Op& erf_op_;
const Op& tanh_op_;
};
diff --git a/src/relay/transforms/pattern_util.h
b/src/relay/transforms/pattern_util.h
index 350d9e1..cd2af9f 100644
--- a/src/relay/transforms/pattern_util.h
+++ b/src/relay/transforms/pattern_util.h
@@ -322,6 +322,11 @@ inline Expr FastExp(Expr e) {
return Call(op, {e});
}
+inline Expr FastErf(Expr e) {
+ static const Op& op = Op::Get("fast_erf");
+ return Call(op, {e});
+}
+
inline Expr FastTanh(Expr e) {
static const Op& op = Op::Get("fast_tanh");
return Call(op, {e});
diff --git a/tests/python/relay/test_op_fast_math.py
b/tests/python/relay/test_op_fast_math.py
index 1d661c3..215b83e 100644
--- a/tests/python/relay/test_op_fast_math.py
+++ b/tests/python/relay/test_op_fast_math.py
@@ -15,6 +15,8 @@
# specific language governing permissions and limitations
# under the License.
import numpy as np
+import scipy
+from scipy import special
import tvm
[GitHub] [incubator-tvm] anijain2305 merged pull request #5241: [Relay][OP] Add fast_erf implementation
anijain2305 merged pull request #5241: [Relay][OP] Add fast_erf implementation URL: https://github.com/apache/incubator-tvm/pull/5241 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] icemelon9 commented on issue #5241: [Relay][OP] Add fast_erf implementation
icemelon9 commented on issue #5241: [Relay][OP] Add fast_erf implementation URL: https://github.com/apache/incubator-tvm/pull/5241#issuecomment-610549195 @anijain2305 ci is now green. Could you take a look 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 With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens
masahi commented on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens URL: https://github.com/apache/incubator-tvm/pull/5262#issuecomment-610518146 I'll revive my old PR https://github.com/apache/incubator-tvm/pull/4741 which should become the first use of composite in the code base. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated (7902f76 -> 869b718)
This is an automated email from the ASF dual-hosted git repository. kevinthesun pushed a change to branch master in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git. from 7902f76 Fixed typo and type mismatch (#5259) add 869b718 [TIR] Fix perf regression of tir refactor (#5258) No new revisions were added by this update. Summary of changes: python/tvm/driver/build_module.py | 2 +- python/tvm/testing.py | 2 +- src/driver/driver_api.cc| 2 +- tests/python/unittest/test_target_codegen_static_init.py| 2 +- tests/python/unittest/test_target_codegen_vm_basic.py | 2 +- tests/python/unittest/test_tir_transform_make_packed_api.py | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-)
[GitHub] [incubator-tvm] kevinthesun merged pull request #5258: [TIR] Fix perf regression of tir refactor
kevinthesun merged pull request #5258: [TIR] Fix perf regression of tir refactor URL: https://github.com/apache/incubator-tvm/pull/5258 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] alexbooth commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
alexbooth commented on issue #5261: [RELAY][BYOC] Add support for composite
functions in BYOC
URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610509436
Partitioning looks good for me with a simple pattern, conv2d_bias_relu. I'm
seeing the check fail in IsOp for composite functions however. Maybe for
another PR.
```c++
bool IsOp(const CallNode* call, const std::string& op_name) const {
const auto* op_node = call->op.as();
CHECK(op_node) << "Expects a single op.";
Op op = GetRef(op_node);
return op == Op::Get(op_name);
}
```
https://github.com/mbaret/tvm/blob/a197bca20a8effc48d06388190aa1b5f3b525ef6/src/relay/backend/contrib/codegen_c/codegen_c.h#L191-L205
```TVMError: Check failed: op_node: Expects a single op.```
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[GitHub] [incubator-tvm] siju-samuel opened a new pull request #5263: [PYTORCH]celu, gelu, selu activations
siju-samuel opened a new pull request #5263: [PYTORCH]celu, gelu, selu activations URL: https://github.com/apache/incubator-tvm/pull/5263 @masahi please help to review activations of pytorch. Thanks in advance. -celu -gelu -selu -some datatype conversion issues also fixed. -testcase coverage added This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
masahi commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610495383 cc @soiferj This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] wpan11nv commented on a change in pull request #5226: [CODEGEN][CUDA] Fix vector load
wpan11nv commented on a change in pull request #5226: [CODEGEN][CUDA] Fix
vector load
URL: https://github.com/apache/incubator-tvm/pull/5226#discussion_r404948586
##
File path: src/target/source/codegen_cuda.cc
##
@@ -591,13 +591,17 @@ void CodeGenCUDA::VisitExpr_(const RampNode* op,
std::ostream& os) {
}
void CodeGenCUDA::VisitExpr_(const BroadcastNode* op, std::ostream& os) { //
NOLINT(*)
- if (op->dtype.is_int() && op->dtype.bits() == 8 && op->lanes == 4) {
+ if ((op->dtype.is_int() || op->dtype.is_uint()) && op->dtype.bits() == 8 &&
op->lanes == 4) {
// make_int8x4
const int64_t *p = as_const_int(op->value);
CHECK(p);
int64_t v = *p & 0xFF;
v = (v << 24) | (v << 16) | (v << 8) | v;
-os << "(int)" << v;
+if (op->dtype.is_uint()) {
Review comment:
why do we care the signedness? this just downcasts to 32 bits,.
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[GitHub] [incubator-tvm] tqchen closed issue #5260: tvm 0.7 only work in python3.6
tqchen closed issue #5260: tvm 0.7 only work in python3.6 URL: https://github.com/apache/incubator-tvm/issues/5260 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] tqchen commented on issue #5260: tvm 0.7 only work in python3.6
tqchen commented on issue #5260: tvm 0.7 only work in python3.6 URL: https://github.com/apache/incubator-tvm/issues/5260#issuecomment-610468392 This is an expected behavior, as we will start to require python3.6 in 0.7 For otehr questions, please open a new thread on https://discuss.tvm.ai/ This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-tvm] branch master updated: Fixed typo and type mismatch (#5259)
This is an automated email from the ASF dual-hosted git repository. zhic 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 7902f76 Fixed typo and type mismatch (#5259) 7902f76 is described below commit 7902f7627fb364abc9c5913ab1838872d0dc1d7d Author: Adrian Muresan AuthorDate: Tue Apr 7 17:21:23 2020 +0200 Fixed typo and type mismatch (#5259) Co-authored-by: Adrian Muresan --- tutorials/dev/relay_pass_infra.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/dev/relay_pass_infra.py b/tutorials/dev/relay_pass_infra.py index b54ac13..6b844ff 100644 --- a/tutorials/dev/relay_pass_infra.py +++ b/tutorials/dev/relay_pass_infra.py @@ -216,13 +216,13 @@ class CustomPipeline: obj = self class ReplaceConstant(tvm.relay.ExprMutator): -def visit_const(self, c): +def visit_constant(self, c): return relay.multiply(obj.multiplier, c) return ReplaceConstant().visit(func) f = example() mod = tvm.IRModule.from_expr(f) -custom_pass = CustomPipeline(multiplier=relay.const(3, "float")) +custom_pass = CustomPipeline(multiplier=relay.const(3, "float32")) assert custom_pass.info.name == "CustomPipeline" mod3 = custom_pass(mod) print(mod3)
[GitHub] [incubator-tvm] zhiics merged pull request #5259: [Relay][Tutorial][Fix] Fixed typo and type mismatch in relay infrastructure tutorial
zhiics merged pull request #5259: [Relay][Tutorial][Fix] Fixed typo and type mismatch in relay infrastructure tutorial URL: https://github.com/apache/incubator-tvm/pull/5259 This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kparzysz-quic commented on a change in pull request #5252: [RUNTIME] Initial implementation of Hexagon runtime support
kparzysz-quic commented on a change in pull request #5252: [RUNTIME] Initial
implementation of Hexagon runtime support
URL: https://github.com/apache/incubator-tvm/pull/5252#discussion_r404894728
##
File path: src/runtime/hexagon/target/hexagon_device_target.cc
##
@@ -0,0 +1,525 @@
+/*
+ * 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.
+ */
+
+#ifdef __ANDROID__
+
+#include
+
+#include
+#include
+#include
+#include
+#include
+
+#include "../hexagon_module.h"
+#include "AEEStdErr.h"
+#include "fastrpc/tvm_hexagon_remote.h"
+#include "hexagon_dsprpcapi.h"
+#include "hexagon_stubapi.h"
+#include "hexagon_target_log.h"
+#include "remote64.h"
+#include "rpcmem.h"
+
+#pragma weak remote_session_control
+
+#define RPCMEM_HEAP 25
+
+// All log messages start with "HexagonTarget::%s", where %s is replaced
+// with the function name, so create macros that add that to avoid repetition.
+// The downside is that the format string must be given as a string literal,
+// but it seems to be a minor issue.
+#define VA_EXPANDER(...) , ##__VA_ARGS__
+#define TVM_LOGD_HT(fmt, ...) \
+ TVM_LOGD("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+#define TVM_LOGE_HT(fmt, ...) \
+ TVM_LOGE("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+
+namespace tvm {
+namespace runtime {
+namespace hexagon {
+
+static constexpr int kStackSize = 128 * 1024; // 128kB stack
+
+class HexagonTarget : public tvm::runtime::hexagon::Device {
+ public:
+ HexagonTarget() {}
+ ~HexagonTarget() final {}
+ void* Alloc(unsigned size, unsigned align) final;
+ void Free(void* ptr) final;
+ void* AllocVtcm(unsigned size, unsigned align) final;
+ void FreeVtcm(void* ptr) final;
+ void CopyDeviceToDevice(void* dst, const void* src, unsigned len) final;
+ void CopyDeviceToHost(void* host_dst, const void* src, unsigned len) final;
+ void CopyHostToDevice(void* dst, const void* host_src, unsigned len) final;
+ void* Load(const std::string& data, const std::string& fmt) final;
+ void Unload(void* mod) final;
+ void* Resolve(const std::string& sym) final;
+ void Call(void* func, uint32_t* scalar, unsigned scalar_num, uint32_t* stack,
+unsigned stack_num) final;
+
+ private:
+ std::pair AddAddrMapping(const void* dsp_addr,
+ void* apps_addr, size_t size);
+ std::pair GetAppsAddr(const void* dsp_addr, bool exact) const;
+ void RemoveAddrMapping(const void* dsp_addr);
+ int OpenDomainChannel(bool set_unsigned_pd);
+ int CloseDomainChannel();
+ void ReleaseLibrary();
+ void FreeMemoryBeforeChannelClose();
+
+ // Mapping from a DSP address to a pair .
+ // Using void* pointers is ok, since DSP pointers will always fit
+ // in apps's pointers, i.e. sizeof_dsp(void*) <= sizeof_apps(void*).
+ std::map> dsp_to_apps_;
+ std::map> vtcm_addr_;
+ remote_handle64 domain_channel_handle_ = AEE_EUNKNOWN;
+ tvm_hexagon_remote_handle_t module_pointer_ = AEE_EUNKNOWN;
+ uint64_t count_channel_open_ = 0;
+ // Global lock, used for all critical sections. This can be refined
+ // in the future.
+ mutable std::mutex crit_section_;
+};
+
+std::shared_ptr CreateHexagonTarget() {
+ return std::make_shared();
+}
+
+std::pair HexagonTarget::AddAddrMapping(const void* dsp_addr,
+ void* apps_addr,
+ size_t size) {
+ crit_section_.lock();
+ auto p = dsp_to_apps_.insert({dsp_addr, {apps_addr, size}});
+ crit_section_.unlock();
+ if (!p.second) {
+TVM_LOGE_HT(
+"failed to insert address mapping: dsp:%p -> apps:%p, size:%zu",
+dsp_addr, apps_addr, size);
+return std::make_pair(nullptr, 0);
+ }
+ TVM_LOGD_HT("added address mapping: dsp:%p -> apps:%p, size:%zu", dsp_addr,
+ apps_addr, size);
+ return p.first->second;
+}
+
+void HexagonTarget::RemoveAddrMapping(const void* dsp_addr) {
+ crit_section_.lock();
+ auto f = dsp_to_apps_.find(dsp_addr);
+ if (f == dsp_to_apps_.end()) {
+TVM_LOGE_HT("failed to remove address mapping for dsp:%p", dsp_addr);
+crit_section_.unlock();
+return;
+ }
+ dsp_to_apps_.erase(f);
+ crit_section_.unlock();
+}
+
[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #5252: [RUNTIME] Initial implementation of Hexagon runtime support
FrozenGene commented on a change in pull request #5252: [RUNTIME] Initial
implementation of Hexagon runtime support
URL: https://github.com/apache/incubator-tvm/pull/5252#discussion_r404855401
##
File path: src/runtime/hexagon/target/hexagon_device_target.cc
##
@@ -0,0 +1,525 @@
+/*
+ * 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.
+ */
+
+#ifdef __ANDROID__
+
+#include
+
+#include
+#include
+#include
+#include
+#include
+
+#include "../hexagon_module.h"
+#include "AEEStdErr.h"
+#include "fastrpc/tvm_hexagon_remote.h"
+#include "hexagon_dsprpcapi.h"
+#include "hexagon_stubapi.h"
+#include "hexagon_target_log.h"
+#include "remote64.h"
+#include "rpcmem.h"
+
+#pragma weak remote_session_control
+
+#define RPCMEM_HEAP 25
+
+// All log messages start with "HexagonTarget::%s", where %s is replaced
+// with the function name, so create macros that add that to avoid repetition.
+// The downside is that the format string must be given as a string literal,
+// but it seems to be a minor issue.
+#define VA_EXPANDER(...) , ##__VA_ARGS__
+#define TVM_LOGD_HT(fmt, ...) \
+ TVM_LOGD("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+#define TVM_LOGE_HT(fmt, ...) \
+ TVM_LOGE("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+
+namespace tvm {
+namespace runtime {
+namespace hexagon {
+
+static constexpr int kStackSize = 128 * 1024; // 128kB stack
+
+class HexagonTarget : public tvm::runtime::hexagon::Device {
+ public:
+ HexagonTarget() {}
+ ~HexagonTarget() final {}
+ void* Alloc(unsigned size, unsigned align) final;
+ void Free(void* ptr) final;
+ void* AllocVtcm(unsigned size, unsigned align) final;
+ void FreeVtcm(void* ptr) final;
+ void CopyDeviceToDevice(void* dst, const void* src, unsigned len) final;
+ void CopyDeviceToHost(void* host_dst, const void* src, unsigned len) final;
+ void CopyHostToDevice(void* dst, const void* host_src, unsigned len) final;
+ void* Load(const std::string& data, const std::string& fmt) final;
+ void Unload(void* mod) final;
+ void* Resolve(const std::string& sym) final;
+ void Call(void* func, uint32_t* scalar, unsigned scalar_num, uint32_t* stack,
+unsigned stack_num) final;
+
+ private:
+ std::pair AddAddrMapping(const void* dsp_addr,
+ void* apps_addr, size_t size);
+ std::pair GetAppsAddr(const void* dsp_addr, bool exact) const;
+ void RemoveAddrMapping(const void* dsp_addr);
+ int OpenDomainChannel(bool set_unsigned_pd);
+ int CloseDomainChannel();
+ void ReleaseLibrary();
+ void FreeMemoryBeforeChannelClose();
+
+ // Mapping from a DSP address to a pair .
+ // Using void* pointers is ok, since DSP pointers will always fit
+ // in apps's pointers, i.e. sizeof_dsp(void*) <= sizeof_apps(void*).
+ std::map> dsp_to_apps_;
+ std::map> vtcm_addr_;
+ remote_handle64 domain_channel_handle_ = AEE_EUNKNOWN;
+ tvm_hexagon_remote_handle_t module_pointer_ = AEE_EUNKNOWN;
+ uint64_t count_channel_open_ = 0;
+ // Global lock, used for all critical sections. This can be refined
+ // in the future.
+ mutable std::mutex crit_section_;
+};
+
+std::shared_ptr CreateHexagonTarget() {
+ return std::make_shared();
+}
+
+std::pair HexagonTarget::AddAddrMapping(const void* dsp_addr,
+ void* apps_addr,
+ size_t size) {
+ crit_section_.lock();
+ auto p = dsp_to_apps_.insert({dsp_addr, {apps_addr, size}});
+ crit_section_.unlock();
+ if (!p.second) {
+TVM_LOGE_HT(
+"failed to insert address mapping: dsp:%p -> apps:%p, size:%zu",
+dsp_addr, apps_addr, size);
+return std::make_pair(nullptr, 0);
+ }
+ TVM_LOGD_HT("added address mapping: dsp:%p -> apps:%p, size:%zu", dsp_addr,
+ apps_addr, size);
+ return p.first->second;
+}
+
+void HexagonTarget::RemoveAddrMapping(const void* dsp_addr) {
+ crit_section_.lock();
+ auto f = dsp_to_apps_.find(dsp_addr);
+ if (f == dsp_to_apps_.end()) {
+TVM_LOGE_HT("failed to remove address mapping for dsp:%p", dsp_addr);
+crit_section_.unlock();
+return;
+ }
+ dsp_to_apps_.erase(f);
+ crit_section_.unlock();
+}
+
[GitHub] [incubator-tvm] mbaret commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC
mbaret commented on issue #5261: [RELAY][BYOC] Add support for composite functions in BYOC URL: https://github.com/apache/incubator-tvm/pull/5261#issuecomment-610392585 cc @masahi @zhiics @trevor-m This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] mbaret commented on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens
mbaret commented on issue #5262: [RELAY][BYOC] Register pattern tables from external codegens URL: https://github.com/apache/incubator-tvm/pull/5262#issuecomment-610392358 cc @masahi @zhiics @trevor-m This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kparzysz-quic commented on issue #5252: [RUNTIME] Initial implementation of Hexagon runtime support
kparzysz-quic commented on issue #5252: [RUNTIME] Initial implementation of Hexagon runtime support URL: https://github.com/apache/incubator-tvm/pull/5252#issuecomment-610391857 The IDL file and the implementation of the Hexagon-side target code will be upstreamed in an upcoming PR. This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [incubator-tvm] kparzysz-quic commented on a change in pull request #5252: [RUNTIME] Initial implementation of Hexagon runtime support
kparzysz-quic commented on a change in pull request #5252: [RUNTIME] Initial
implementation of Hexagon runtime support
URL: https://github.com/apache/incubator-tvm/pull/5252#discussion_r404814243
##
File path: src/runtime/hexagon/target/hexagon_device_target.cc
##
@@ -0,0 +1,525 @@
+/*
+ * 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.
+ */
+
+#ifdef __ANDROID__
+
+#include
+
+#include
+#include
+#include
+#include
+#include
+
+#include "../hexagon_module.h"
+#include "AEEStdErr.h"
+#include "fastrpc/tvm_hexagon_remote.h"
+#include "hexagon_dsprpcapi.h"
+#include "hexagon_stubapi.h"
+#include "hexagon_target_log.h"
+#include "remote64.h"
+#include "rpcmem.h"
+
+#pragma weak remote_session_control
+
+#define RPCMEM_HEAP 25
+
+// All log messages start with "HexagonTarget::%s", where %s is replaced
+// with the function name, so create macros that add that to avoid repetition.
+// The downside is that the format string must be given as a string literal,
+// but it seems to be a minor issue.
+#define VA_EXPANDER(...) , ##__VA_ARGS__
+#define TVM_LOGD_HT(fmt, ...) \
+ TVM_LOGD("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+#define TVM_LOGE_HT(fmt, ...) \
+ TVM_LOGE("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+
+namespace tvm {
+namespace runtime {
+namespace hexagon {
+
+static constexpr int kStackSize = 128 * 1024; // 128kB stack
+
+class HexagonTarget : public tvm::runtime::hexagon::Device {
+ public:
+ HexagonTarget() {}
+ ~HexagonTarget() final {}
+ void* Alloc(unsigned size, unsigned align) final;
+ void Free(void* ptr) final;
+ void* AllocVtcm(unsigned size, unsigned align) final;
+ void FreeVtcm(void* ptr) final;
+ void CopyDeviceToDevice(void* dst, const void* src, unsigned len) final;
+ void CopyDeviceToHost(void* host_dst, const void* src, unsigned len) final;
+ void CopyHostToDevice(void* dst, const void* host_src, unsigned len) final;
+ void* Load(const std::string& data, const std::string& fmt) final;
+ void Unload(void* mod) final;
+ void* Resolve(const std::string& sym) final;
+ void Call(void* func, uint32_t* scalar, unsigned scalar_num, uint32_t* stack,
+unsigned stack_num) final;
+
+ private:
+ std::pair AddAddrMapping(const void* dsp_addr,
+ void* apps_addr, size_t size);
+ std::pair GetAppsAddr(const void* dsp_addr, bool exact) const;
+ void RemoveAddrMapping(const void* dsp_addr);
+ int OpenDomainChannel(bool set_unsigned_pd);
+ int CloseDomainChannel();
+ void ReleaseLibrary();
+ void FreeMemoryBeforeChannelClose();
+
+ // Mapping from a DSP address to a pair .
+ // Using void* pointers is ok, since DSP pointers will always fit
+ // in apps's pointers, i.e. sizeof_dsp(void*) <= sizeof_apps(void*).
+ std::map> dsp_to_apps_;
+ std::map> vtcm_addr_;
+ remote_handle64 domain_channel_handle_ = AEE_EUNKNOWN;
+ tvm_hexagon_remote_handle_t module_pointer_ = AEE_EUNKNOWN;
+ uint64_t count_channel_open_ = 0;
+ // Global lock, used for all critical sections. This can be refined
+ // in the future.
+ mutable std::mutex crit_section_;
+};
+
+std::shared_ptr CreateHexagonTarget() {
+ return std::make_shared();
+}
+
+std::pair HexagonTarget::AddAddrMapping(const void* dsp_addr,
+ void* apps_addr,
+ size_t size) {
+ crit_section_.lock();
+ auto p = dsp_to_apps_.insert({dsp_addr, {apps_addr, size}});
+ crit_section_.unlock();
+ if (!p.second) {
+TVM_LOGE_HT(
+"failed to insert address mapping: dsp:%p -> apps:%p, size:%zu",
+dsp_addr, apps_addr, size);
+return std::make_pair(nullptr, 0);
+ }
+ TVM_LOGD_HT("added address mapping: dsp:%p -> apps:%p, size:%zu", dsp_addr,
+ apps_addr, size);
+ return p.first->second;
+}
+
+void HexagonTarget::RemoveAddrMapping(const void* dsp_addr) {
+ crit_section_.lock();
+ auto f = dsp_to_apps_.find(dsp_addr);
+ if (f == dsp_to_apps_.end()) {
+TVM_LOGE_HT("failed to remove address mapping for dsp:%p", dsp_addr);
+crit_section_.unlock();
+return;
+ }
+ dsp_to_apps_.erase(f);
+ crit_section_.unlock();
+}
+
[GitHub] [incubator-tvm] kparzysz-quic commented on a change in pull request #5252: [RUNTIME] Initial implementation of Hexagon runtime support
kparzysz-quic commented on a change in pull request #5252: [RUNTIME] Initial
implementation of Hexagon runtime support
URL: https://github.com/apache/incubator-tvm/pull/5252#discussion_r404783723
##
File path: src/runtime/hexagon/target/hexagon_device_target.cc
##
@@ -0,0 +1,525 @@
+/*
+ * 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.
+ */
+
+#ifdef __ANDROID__
+
+#include
+
+#include
+#include
+#include
+#include
+#include
+
+#include "../hexagon_module.h"
+#include "AEEStdErr.h"
+#include "fastrpc/tvm_hexagon_remote.h"
+#include "hexagon_dsprpcapi.h"
+#include "hexagon_stubapi.h"
+#include "hexagon_target_log.h"
+#include "remote64.h"
+#include "rpcmem.h"
+
+#pragma weak remote_session_control
+
+#define RPCMEM_HEAP 25
+
+// All log messages start with "HexagonTarget::%s", where %s is replaced
+// with the function name, so create macros that add that to avoid repetition.
+// The downside is that the format string must be given as a string literal,
+// but it seems to be a minor issue.
+#define VA_EXPANDER(...) , ##__VA_ARGS__
+#define TVM_LOGD_HT(fmt, ...) \
+ TVM_LOGD("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+#define TVM_LOGE_HT(fmt, ...) \
+ TVM_LOGE("HexagonTarget::%s: " fmt, __func__ VA_EXPANDER(__VA_ARGS__))
+
+namespace tvm {
+namespace runtime {
+namespace hexagon {
+
+static constexpr int kStackSize = 128 * 1024; // 128kB stack
+
+class HexagonTarget : public tvm::runtime::hexagon::Device {
+ public:
+ HexagonTarget() {}
+ ~HexagonTarget() final {}
+ void* Alloc(unsigned size, unsigned align) final;
+ void Free(void* ptr) final;
+ void* AllocVtcm(unsigned size, unsigned align) final;
+ void FreeVtcm(void* ptr) final;
+ void CopyDeviceToDevice(void* dst, const void* src, unsigned len) final;
+ void CopyDeviceToHost(void* host_dst, const void* src, unsigned len) final;
+ void CopyHostToDevice(void* dst, const void* host_src, unsigned len) final;
+ void* Load(const std::string& data, const std::string& fmt) final;
+ void Unload(void* mod) final;
+ void* Resolve(const std::string& sym) final;
+ void Call(void* func, uint32_t* scalar, unsigned scalar_num, uint32_t* stack,
+unsigned stack_num) final;
+
+ private:
+ std::pair AddAddrMapping(const void* dsp_addr,
+ void* apps_addr, size_t size);
+ std::pair GetAppsAddr(const void* dsp_addr, bool exact) const;
+ void RemoveAddrMapping(const void* dsp_addr);
+ int OpenDomainChannel(bool set_unsigned_pd);
+ int CloseDomainChannel();
+ void ReleaseLibrary();
+ void FreeMemoryBeforeChannelClose();
+
+ // Mapping from a DSP address to a pair .
+ // Using void* pointers is ok, since DSP pointers will always fit
+ // in apps's pointers, i.e. sizeof_dsp(void*) <= sizeof_apps(void*).
+ std::map> dsp_to_apps_;
+ std::map> vtcm_addr_;
+ remote_handle64 domain_channel_handle_ = AEE_EUNKNOWN;
+ tvm_hexagon_remote_handle_t module_pointer_ = AEE_EUNKNOWN;
+ uint64_t count_channel_open_ = 0;
+ // Global lock, used for all critical sections. This can be refined
+ // in the future.
+ mutable std::mutex crit_section_;
+};
+
+std::shared_ptr CreateHexagonTarget() {
+ return std::make_shared();
+}
+
+std::pair HexagonTarget::AddAddrMapping(const void* dsp_addr,
+ void* apps_addr,
+ size_t size) {
+ crit_section_.lock();
+ auto p = dsp_to_apps_.insert({dsp_addr, {apps_addr, size}});
+ crit_section_.unlock();
+ if (!p.second) {
+TVM_LOGE_HT(
+"failed to insert address mapping: dsp:%p -> apps:%p, size:%zu",
+dsp_addr, apps_addr, size);
+return std::make_pair(nullptr, 0);
+ }
+ TVM_LOGD_HT("added address mapping: dsp:%p -> apps:%p, size:%zu", dsp_addr,
+ apps_addr, size);
+ return p.first->second;
+}
+
+void HexagonTarget::RemoveAddrMapping(const void* dsp_addr) {
+ crit_section_.lock();
+ auto f = dsp_to_apps_.find(dsp_addr);
+ if (f == dsp_to_apps_.end()) {
+TVM_LOGE_HT("failed to remove address mapping for dsp:%p", dsp_addr);
+crit_section_.unlock();
+return;
+ }
+ dsp_to_apps_.erase(f);
+ crit_section_.unlock();
+}
+
