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Merge pull request tensorflow#10772 from Intel-tensorflow/mkl-biasadd…
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…grad-fix

Changing context search for BiasAddGrad rewrite from BFS to stricter check
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@yifeif
yifeif authored Jun 20, 2017
2 parents 9cc14c6 + 059828d commit 9e4fa8e
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Showing 2 changed files with 439 additions and 90 deletions.
277 changes: 193 additions & 84 deletions tensorflow/core/graph/mkl_layout_pass.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -247,16 +247,10 @@ namespace tensorflow {
//
// P = Conv2DWithBiasBackpropBias(O, O_m)
//
// 'Distance' between input of BiasAddGrad and _MklConv2D in terms of hops is
// the context matching depth. If _MklConv2DWithBias is not within the context
// matching depth, then we do not rewrite BiasAddGrad.

// How many hops do we search for matching node in the backward dataflow graph?
// We use maxhop of 10 based on empirical observations. Also, these are
// maxhops in backward data-flow graph. Since input of forward nodes (Conv2D)
// directly goes to backward nodes, we do not expect the hop-distance
// would be more than few nodes.
static size_t kNodeMergeContextMaxDepth = 10;
// Rewrite of BiasAddGrad into Conv2DWithBiasBackpropBias takes place depending
// on the matching 'context'. The term context is loosely related to which
// forward op is _associated_ to BiasAddGrad. If it is _MklConv2DWithBias then
// we consider it Conv2D context; if it is MatMul, then it is MatMul context.

class MklLayoutRewritePass : public GraphOptimizationPass {
public:
Expand All @@ -280,6 +274,8 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
csinfo_.max_pool = "MaxPool";
csinfo_.max_pool_grad = "MaxPoolGrad";
csinfo_.mkl_conv2d = "_MklConv2D";
csinfo_.mkl_conv2d_grad_input = "_MklConv2DBackpropInput";
csinfo_.mkl_conv2d_grad_filter = "_MklConv2DBackpropFilter";
csinfo_.mkl_conv2d_with_bias = "_MklConv2DWithBias";
csinfo_.mkl_conv2d_with_bias_backprop_bias =
"_MklConv2DWithBiasBackpropBias";
Expand Down Expand Up @@ -360,16 +356,12 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
minfo_.push_back({csinfo_.mkl_conv2d, csinfo_.bias_add, 0,
csinfo_.mkl_conv2d_with_bias});

// We use maxhop of 10 based on empirical observations. Also, these are
// maxhops in backward data-flow graph. Since input of forward nodes
// (Conv2D) directly goes to backward nodes, we do not expect the
// hop-distance would be more than few nodes.
biasaddgrad_matmul_context_ = {csinfo_.bias_add_grad, csinfo_.matmul,
kNodeMergeContextMaxDepth};
IsBiasAddGradInMatMulContext};

biasaddgrad_conv2dwithbias_context_ = {csinfo_.bias_add_grad,
csinfo_.mkl_conv2d_with_bias,
kNodeMergeContextMaxDepth};
IsBiasAddGradInConv2DWithBiasContext};

cinfo_.push_back(&biasaddgrad_matmul_context_);
cinfo_.push_back(&biasaddgrad_conv2dwithbias_context_);
Expand All @@ -392,9 +384,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
string node; // Name of the node to be rewritten
string fwd; // Name of the node in the forward pass that this node
// corresponds to
size_t max_hop; // Maximum number of hops the fwd is located
// from this node. If the fwd is farther than max_hop
// then we do not rewrite the node.
std::function<bool(const Node*, const Node**, void* c)> context_match_fn;
} ContextInfo;

/// Structure to specify the name of an original node, its new name after
Expand Down Expand Up @@ -438,7 +428,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {

/// Structure to store all constant strings
/// NOTE: names are alphabetically sorted.
struct {
typedef struct {
string avg_pool;
string avg_pool_grad;
string bias_add;
Expand All @@ -457,13 +447,15 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
string max_pool;
string max_pool_grad;
string mkl_conv2d;
string mkl_conv2d_grad_input;
string mkl_conv2d_grad_filter;
string mkl_conv2d_with_bias;
string mkl_conv2d_with_bias_backprop_bias;
string relu;
string relu_grad;
string reshape;
string split;
} csinfo_;
} ConstStringsInfo;

private:
/// Maintain info about nodes to rewrite
Expand All @@ -478,6 +470,9 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
/// Maintain info about nodes to rewrite
static std::vector<ContextInfo*> cinfo_;

/// Maintain structure of constant strings
static ConstStringsInfo csinfo_;

/// Context variables used in referencing rules
static ContextInfo biasaddgrad_matmul_context_;
static ContextInfo biasaddgrad_conv2dwithbias_context_;
Expand Down Expand Up @@ -629,6 +624,173 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
return false;
}

// Is BiasAddGrad node in 'n' is associated with Conv2DWithBias node
// specified in contextinfo 'ci'. Function updates fwd_node to point
// to Conv2DWithBias node if 'n' is associated with Conv2DWithBias.
//
// Association checks for one of the following graphs:
//
// Graph A:
//
// _ = Conv2DWithBias(F, I, _)
// ..
// _ = Conv2DBackpropFilter(F, _, G)
// _ = Conv2DBackpropInput(_, I, G)
// _ = BiasAddGrad(G)
//
// OR
//
// Graph B:
//
// _ = Conv2DWithBias(F, _, _)
// ..
// _ = Conv2DBackpropFilter(F, _, G)
// _ = BiasAddGrad(G)
//
// Here F, G, and I are graph nodes; _ represents graph nodes that we
// don't care here.
//
// @return - true (if BiasAddGrad is associated with Conv2DWithBias);
// false otherwise.
static bool IsBiasAddGradInConv2DWithBiasContext(const Node* n,
const Node** fwd_node,
void* ci) {
CHECK_NOTNULL(n);
CHECK_NOTNULL(fwd_node);
CHECK_NOTNULL(ci);
*fwd_node = nullptr;

CHECK_EQ(n->type_string(), csinfo_.bias_add_grad);

// Get the only 1 input of BiasAddGrad.
CHECK_EQ(n->num_inputs(), 1);
const Node* bias_add_grad_inp = nullptr;
TF_CHECK_OK(n->input_node(0, &bias_add_grad_inp));
CHECK_NOTNULL(bias_add_grad_inp);

// Check if this input also goes to BackpropFilter and BackpropInput
// as 3rd input.
bool found_backprop_input = false;
bool found_backprop_filter = false;
Node* backprop_filter_node = nullptr;
Node* backprop_input_node = nullptr;

for (const Edge* e : bias_add_grad_inp->out_edges()) {
Node* third_input = nullptr;
if (e->dst()->type_string() == csinfo_.conv2d_grad_input ||
e->dst()->type_string() == csinfo_.mkl_conv2d_grad_input) {
// Third input (index 2) of BackpropInput
TF_CHECK_OK(e->dst()->input_node(2, &third_input));
// Third input (index 2) of BackpropInput must be same as the input
// of BiasAddGrad.
if (third_input == bias_add_grad_inp) {
found_backprop_input = true;
backprop_input_node = e->dst();
}
}

if (e->dst()->type_string() == csinfo_.conv2d_grad_filter ||
e->dst()->type_string() == csinfo_.mkl_conv2d_grad_filter) {
// Third input (index 2) of BackpropFilter
TF_CHECK_OK(e->dst()->input_node(2, &third_input));
// Third input (index 2) of BackpropFilter must be same as the input
// of BiasAddGrad.
if (third_input == bias_add_grad_inp) {
found_backprop_filter = true;
backprop_filter_node = e->dst();
}
}

// If we found both the nodes, then we can stop the search.
if (found_backprop_input && found_backprop_filter) {
break;
}
}

// If BackpropFilter node is not found, then this is not
// Conv2DWithBias context. For 2nd graph in the example above, only
// BackpropFilter would be present.
if (!found_backprop_filter) {
return false;
}

// Otherwise, we found the nodes.
CHECK_NOTNULL(backprop_filter_node);
if (found_backprop_input) {
CHECK_NOTNULL(backprop_input_node);
}

// Now that we confirmed that this is Conv2DWithBias context, we need to
// get access to the forward node (Conv2DWithBias). 2nd input of
// Conv2DWithBias is same as the 2nd input of Conv2DBackpropInput; 1st
// input of Conv2DWithBias is same as the 1st input of Conv2DBackpropFilter
// (This comes from definition of gradient computation for Conv2D).
if (found_backprop_input) {
// Graph A in the example.
Node* second_inp_of_input = nullptr;
Node* first_inp_of_filter = nullptr;
TF_CHECK_OK(backprop_input_node->input_node(1, &second_inp_of_input));
TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
CHECK_NOTNULL(second_inp_of_input);
CHECK_NOTNULL(first_inp_of_filter);

// Now we need to find out Conv2DWithBias node from these input nodes.
// Conv2DWithBias node is the node that accepts both the nodes
// second_inp_of_input and first_inp_of_filter in 2nd and 1st input slots.
for (const Edge* fe : first_inp_of_filter->out_edges()) {
if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
fe->dst_input() == 0) {
for (const Edge* ie : second_inp_of_input->out_edges()) {
if (ie->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
ie->dst_input() == 1 && fe->dst() == ie->dst()) {
VLOG(1) << "MklLayoutRewritePass: found "
<< fe->dst()->DebugString()
<< " as the forward node for matching context, backward"
<< " node is: " << n->DebugString();
*fwd_node = fe->dst();
return true;
}
}
}
}
} else {
// We did not find BackpropInput, so we work with BackpropFilter only.
// Graph B in the example.
Node* first_inp_of_filter = nullptr;
TF_CHECK_OK(backprop_filter_node->input_node(0, &first_inp_of_filter));
CHECK_NOTNULL(first_inp_of_filter);

// Now we need to find out Conv2DWithBias node from first input of
// BackpropFIlter. Conv2DWithBias node is the node that accepts
// first_inp_of_filter in 1st input slot.
for (const Edge* fe : first_inp_of_filter->out_edges()) {
if (fe->dst()->type_string() == csinfo_.mkl_conv2d_with_bias &&
fe->dst_input() == 0) {
VLOG(1) << "MklLayoutRewritePass: found "
<< fe->dst()->DebugString()
<< " as the forward node for matching context, backward"
<< " node is: " << n->DebugString();
*fwd_node = fe->dst();
return true;
}
}
}

return false;
}

// Is BiasAddGrad node in 'n' is associated with MatMul node
// specified in contextinfo 'ci'. Function does not update fwd_node.
//
// @return - true (if BiasAddGrad is associated with MatMul);
// false otherwise.
static bool IsBiasAddGradInMatMulContext(const Node* n,
const Node** fwd_node,
void* ci) {
return (!IsBiasAddGradInConv2DWithBiasContext(n, fwd_node, ci));
}


// Rewrite rule that uses context-information for matching,
// used in scenario 2.
//
Expand All @@ -639,8 +801,6 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
static bool ContextMatchRewrite(const Node* n, const ContextInfo* c);

// Helper function that searches the matching contextinfo for the node.
// Implements depth-first search in the data dependence graph for the
// gradient op in the backward direction.
//
// @input n - Node (gradient op) whose contextinfo is to be searched,
// fwd_node - pointer to node from the forward pass that this node
Expand Down Expand Up @@ -788,6 +948,7 @@ class MklLayoutRewritePass : public GraphOptimizationPass {
Node* orig_node);
};

MklLayoutRewritePass::ConstStringsInfo MklLayoutRewritePass::csinfo_;
MklLayoutRewritePass::ContextInfo
MklLayoutRewritePass::biasaddgrad_conv2dwithbias_context_;
MklLayoutRewritePass::ContextInfo
Expand Down Expand Up @@ -1667,12 +1828,12 @@ Status MklLayoutRewritePass::RewriteNode(std::unique_ptr<Graph>* g,
const ContextInfo* ci = nullptr;
bool is_context_based_rewrite = false;
if ((ci = SearchMatchingContext(orig_node, &fwd_node)) != nullptr) {
CHECK_NOTNULL(fwd_node);
is_context_based_rewrite = true;

// Sanity checks for context-based rewrite (if any)
if (orig_node->type_string() == csinfo_.bias_add_grad &&
ri->new_name == csinfo_.mkl_conv2d_with_bias_backprop_bias) {
CHECK_NOTNULL(fwd_node);
DataType orig_T, ctx_T;
string orig_data_format, ctx_data_format;
TF_CHECK_OK(GetNodeAttr(orig_node->def(), "T", &orig_T));
Expand Down Expand Up @@ -1784,69 +1945,17 @@ MklLayoutRewritePass::SearchMatchingContext(const Node* n,
CHECK_NOTNULL(fwd_node);
*fwd_node = nullptr;

// Search for matching contextinfo based on node name.
// There could be more than one matching contextinfos.
bool is_matching_cinfo_found = false;
std::vector<const ContextInfo*> mci;
// Search for matching contextinfo based on node name and call
// callback function using matching contextinfo.
// There could be more than one matching contextinfos but whichever
// matches first is returned.
for (auto ci = cinfo_.cbegin(); ci != cinfo_.cend(); ++ci) {
if (n->type_string() == (*ci)->node) {
mci.push_back(*ci);
is_matching_cinfo_found = true;
if (n->type_string() == (*ci)->node &&
(*ci)->context_match_fn(n, fwd_node, *ci)) {
VLOG(1) << "Found context as matching: " << (*ci)->fwd;
return *ci;
}
}
// If no matching contextinfo is found, return immediately.
if (!is_matching_cinfo_found) {
return nullptr;
}

VLOG(1) << "MklLayoutRewritePass: Searching graph for: " << n->type_string()
<< " in backwards.";

// Now we will check for forward op name for context info in data
// flow graph. Get the max hops we should search for the fwd node.
// We are now going to search (breadth-first) backwards in data
// dependence graph (for up to max hops) from n for the node
// specified in fwd.
// queue to maintain nodes to be visited and depth info for
// breadth-first search
std::queue<std::pair<const Node*, int>> nqueue;
const Node* curr_node = n;
size_t curr_depth = 0;
nqueue.push(std::make_pair(curr_node, curr_depth));

while (curr_depth < kNodeMergeContextMaxDepth && !nqueue.empty()) {
std::pair<const Node*, int> curr_pair = nqueue.front();
nqueue.pop();

std::set<const Node*> visited_nodes;
curr_node = curr_pair.first;
curr_depth = curr_pair.second;
CHECK_NOTNULL(curr_node);

VLOG(1) << "MklLayoutRewritePass: Visiting node: "
<< curr_node->type_string() << " at depth: " << curr_depth
<< " for node: " << n->type_string();

// If we find a match, we return immediately.
for (const ContextInfo* ci : mci) {
if (curr_node->type_string() == ci->fwd) {
*fwd_node = curr_node;
return ci;
}
}

// Else we explore backward edges from current node.
// Add the source nodes of all incoming edges of the node to the queue.
for (const Edge* e : curr_node->in_edges()) {
// We do not visit already visited node.
if (visited_nodes.find(e->src()) == visited_nodes.end()) {
// Depth of these nodes is 1 more than the depth of current node.
nqueue.push(std::make_pair(e->src(), curr_depth + 1));
visited_nodes.insert(e->src());
}
}
} /* while */

return nullptr;
}

Expand Down
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