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https://github.com/zebrajr/pytorch.git
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c705d9eb1e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17991 changes: -Breaks bc: Tensor::type() now returns DeprecatedTypeProperties& rather than Type&. -Added DeprecatedTypeProperties, it serves as a temporary replacement for Type as the return value of Tensor::type(). This contributes to making Type just for dispatch purposes so that we can make it dtype agnostic. -Tensor::dispatch_type() now returns Type& like Tensor::type() used to do. -Changed callsites of Tensor::type() appropriately. Reviewed By: ezyang Differential Revision: D14443117 fbshipit-source-id: 239ccb7a09626279a71d1a37f8f82e7f57bf7d9e
133 lines
3.5 KiB
C++
133 lines
3.5 KiB
C++
#include <torch/csrc/jit/ir.h>
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#include <algorithm>
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#include <unordered_map>
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#include <ATen/core/functional.h>
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#include <ATen/core/interned_strings.h>
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#include <c10/util/Exception.h>
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#include <torch/csrc/jit/node_hashing.h>
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#include <torch/csrc/jit/passes/common_subexpression_elimination.h>
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#include <torch/csrc/utils/hash.h>
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namespace torch {
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namespace jit {
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namespace {
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bool tensorEqual(const at::Tensor& lhs, const at::Tensor& rhs) {
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return lhs.type() == rhs.type() && lhs.equal(rhs);
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}
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bool tensorListEqual(
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const std::vector<at::Tensor>& lhs,
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const std::vector<at::Tensor>& rhs) {
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if (lhs.size() != rhs.size())
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return false;
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return std::equal(lhs.begin(), lhs.end(), rhs.begin(), tensorEqual);
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}
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// Check whether two nodes have the same attributes in CSE.
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// This function may be too conservative for general use.
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// Do NOT support g/gs attributes.
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bool attributesEqualCSE(const Node* lhs, const Node* rhs) {
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AT_ASSERT(lhs != nullptr);
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AT_ASSERT(rhs != nullptr);
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// One has attributes, the other does not.
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if (lhs->hasAttributes() != rhs->hasAttributes())
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return false;
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// Neither has attributes.
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if (!lhs->hasAttributes() && !rhs->hasAttributes())
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return true;
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auto lnames = lhs->attributeNames();
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auto rnames = rhs->attributeNames();
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std::sort(lnames.begin(), lnames.end());
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std::sort(rnames.begin(), rnames.end());
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if (lnames != rnames)
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return false;
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for (auto name : lnames) {
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if (lhs->kindOf(name) != rhs->kindOf(name))
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return false;
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#define COMPARE_ATTRIBUTEVALUE(type) \
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case AttributeKind::type: { \
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if (lhs->type(name) != rhs->type(name)) \
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return false; \
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} break;
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switch (lhs->kindOf(name)) {
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COMPARE_ATTRIBUTEVALUE(f)
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COMPARE_ATTRIBUTEVALUE(fs)
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COMPARE_ATTRIBUTEVALUE(i)
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COMPARE_ATTRIBUTEVALUE(is)
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COMPARE_ATTRIBUTEVALUE(s)
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COMPARE_ATTRIBUTEVALUE(ss)
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case AttributeKind::t: {
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if (!tensorEqual(lhs->t(name), rhs->t(name)))
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return false;
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break;
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}
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case AttributeKind::ts: {
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if (!tensorListEqual(lhs->ts(name), rhs->ts(name)))
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return false;
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break;
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}
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case AttributeKind::g:
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case AttributeKind::gs:
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return false;
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}
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#undef COMPARE_ATTRIBUTEVALUE
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}
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return true;
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}
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} // anonymous namespace
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size_t HashNode::operator()(const Node* k) const {
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AT_ASSERT(k != nullptr);
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return get_hash(
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k->kind(),
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fmap(k->outputs(), [](const Value* v) { return v->type()->kind(); }),
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fmap(k->inputs(), [](const Value* v) { return v->unique(); }));
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};
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bool EqualNode::operator()(const Node* lhs, const Node* rhs) const {
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if (lhs == nullptr && rhs == nullptr)
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return true;
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if (lhs == nullptr || rhs == nullptr)
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return false;
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if (lhs->kind() != rhs->kind())
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return false;
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// Check whether the output types are the same.
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auto lhs_outputs = lhs->outputs();
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auto rhs_outputs = rhs->outputs();
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if (lhs_outputs.size() != rhs_outputs.size())
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return false;
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for (size_t i = 0; i < lhs_outputs.size(); ++i) {
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if (*lhs_outputs[i]->type() != *rhs_outputs[i]->type())
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return false;
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}
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// Check whether the inputs are the same.
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auto lhs_inputs = lhs->inputs();
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auto rhs_inputs = rhs->inputs();
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if (lhs_inputs.size() != rhs_inputs.size())
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return false;
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if (!std::equal(lhs_inputs.begin(), lhs_inputs.end(), rhs_inputs.begin()))
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return false;
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if (!attributesEqualCSE(lhs, rhs))
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return false;
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return true;
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};
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} // namespace jit
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} // namespace torch
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