8000 Fuse MatMul and Add into Gemm (#1542) · onnx/onnx@f85221f · GitHub
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Fuse MatMul and Add into Gemm (#1542)
* Fuse MatMul and add into Gemm * Fix signed & unsigned comparison * Fix whitespace in optimizer_test * Typecheck fix * Add MatMul symbol * Remove unnecessary print statement * Remove unnecessary check * Additional tests for fuse_matmul_add_bias_into_gemm * Compare graphs instead of nodes with asserts * Additional shape checks and test fixes * Minor style fix * Simpler check of shape compatibility * Reintroduce more strict shape checks
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‎onnx/common/interned_strings.h

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@@ -40,6 +40,7 @@ namespace ONNX_NAMESPACE {
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_(Squeeze) \
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_(Undefined) \
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_(FusionGroup) \
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_(MatMul) \
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_(Gemm) \
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_(Tile) \
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_(SubConstant) \

‎onnx/examples/optimize_onnx.ipynb

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@@ -63,6 +63,7 @@
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"\tfuse_consecutive_reduce_unsqueeze\n",
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"\tfuse_consecutive_squeezes\n",
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"\tfuse_consecutive_transposes\n",
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"\tfuse_matmul_add_bias_into_gemm\n",
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"\tfuse_pad_into_conv\n",
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"\tfuse_transpose_into_gemm\n",
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"\tlift_lexical_references\n",

‎onnx/optimizer/pass_registry.h

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@@ -15,13 +15,14 @@
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#include "onnx/optimizer/passes/eliminate_unused_initializer.h"
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#include "onnx/optimizer/passes/extract_constant_to_initializer.h"
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#include "onnx/optimizer/passes/fuse_add_bias_into_conv.h"
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#include "onnx/optimizer/passes/fuse_pad_into_conv.h"
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#include "onnx/optimizer/passes/fuse_bn_into_conv.h"
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#include "onnx/optimizer/passes/fuse_consecutive_concats.h"
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#include "onnx/optimizer/passes/fuse_consecutive_log_softmax.h"
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#include "onnx/optimizer/passes/fuse_consecutive_reduce_unsqueeze.h"
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#include "onnx/optimizer/passes/fuse_consecutive_squeezes.h"
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#include "onnx/optimizer/passes/fuse_consecutive_transposes.h"
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#include "onnx/optimizer/passes/fuse_matmul_add_bias_into_gemm.h"
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#include "onnx/optimizer/passes/fuse_pad_into_conv.h"
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#include "onnx/optimizer/passes/fuse_transpose_into_gemm.h"
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#include "onnx/optimizer/passes/lift_lexical_references.h"
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#include "onnx/optimizer/passes/nop.h"
@@ -50,13 +51,14 @@ struct GlobalPassRegistry {
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registerPass<EliminateUnusedInitializer>();
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registerPass<ExtractConstantToInitializer>();
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registerPass<FuseAddBiasIntoConv>();
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registerPass<FusePadIntoConv>();
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registerPass<FuseBNIntoConv>();
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registerPass<FuseConsecutiveConcats>();
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registerPass<FuseConsecutiveLogSoftmax>();
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registerPass<FuseConsecutiveReduceUnsqueeze>();
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registerPass<FuseConsecutiveSqueezes>();
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registerPass<FuseConsecutiveTransposes>();
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registerPass<FuseMatMulAddBiasIntoGemm>();
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registerPass<FusePadIntoConv>();
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registerPass<FuseTransposeIntoGemm>();
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registerPass<LiftLexicalReferences>();
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registerPass<SplitInit>();

‎onnx/optimizer/passes/fuse_matmul_add_bias_into_gemm.h

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@@ -0,0 +1,107 @@
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// ATTENTION: The code in this file is highly EXPERIMENTAL.
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// Adventurous users should note that the APIs will probably change.
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#pragma once
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// Before:
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// Z = MatMul(X, Y)
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// A = Z + Bias
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// After:
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// A = Gemm(X, Y, Bias)
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//
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// the pass can handle th F438 e case when:
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// case 1: Bias is 1D tensor and Bias.dim[0] == Z.dim[1]
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// case 2: Bias is 2D tensor and Bias.dim[0] == Z.dim[0] or 1
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// and Bias.dim[1] = Z.dim[1]
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#include <numeric>
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#include "onnx/common/assertions.h"
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#include "onnx/optimizer/pass.h"
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namespace ONNX_NAMESPACE {
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namespace optimization {
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struct FuseMatMulAddBiasIntoGemm final : public PredicateBasedPass {
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explicit FuseMatMulAddBiasIntoGemm()
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: PredicateBasedPass(
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PassType::Fuse,
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PassEfficiency::Complete,
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PassOptimizationType::Compute) {}
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std::string getPassName() const override {
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return "fuse_matmul_add_bias_into_gemm";
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}
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bool patternMatchPredicate(Node* node) override {
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return node->kind() == kAdd &&
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node->inputs()[0]->node()->kind() == kMatMul;
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}
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bool runTransform(Node* n, Graph& graph, NodeDestroyType& destroy_current)
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override {
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// due to current broadcasting's constraint, MatMul has to be the first
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// operand
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destroy_current = NodeDestroyType::DestroyZero;
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auto orig_matmul = n->inputs()[0];
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auto orig_bias = n->inputs()[1];
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// check if bias is Const or in graph's initializers
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if (orig_bias->node()->kind() != kConstant &&
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orig_bias->node()->kind() != kParam) {
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return false;
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}
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// check if MatMul is only used by Add
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if (orig_matmul->uses().size() > 1) {
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return false;
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}
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auto x_shape = orig_matmul->node()->inputs()[0]->sizes();
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auto y_shape = orig_matmul->node()->inputs()[1]->sizes();
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int64_t z_N = -1;
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int64_t z_M = -1;
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// try to get feature N from x_shape
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if (static_cast<int64_t>(x_shape.size()) == 2 && x_shape[0].is_int) {
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z_N = x_shape[0].dim;
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} else {
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return false;
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}
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// try to get feature M from y_shape
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if (static_cast<int64_t>(y_shape.size()) == 2 && y_shape[1].is_int) {
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z_M = y_shape[1].dim;
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} else {
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return false;
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}
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// check if bias_shape is compatible
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auto bias_shape = orig_bias->sizes();
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auto bias_dim = static_cast<int64_t>(bias_shape.size());
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int64_t bias_N = -1;
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int64_t bias_M = -1;
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if (bias_dim == 1 && bias_shape[0].is_int) {
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bias_N = 1;
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bias_M = bias_shape[0].dim;
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} else if (bias_dim == 2 && bias_shape[0].is_int && bias_shape[1].is_int) {
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bias_N = bias_shape[0].dim;
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bias_M = bias_shape[1].dim;
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} else {
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return false;
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}
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if ((bias_N != z_N && bias_N != 1) || bias_M != z_M) {
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return false;
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}
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// proceed to fuse MatMul and Add into Gemm
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Node* gemm = graph.create(kGemm,
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orig_matmul->node()->inputs(),
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n->outputs().size());
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gemm->addInput(n->inputs()[1]);
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for (int i = 0; i < static_cast<int64_t>(gemm->outputs().size()); ++i) {
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gemm->outputs()[i]->copyMetadata(n->outputs()[i]);
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}
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gemm->f_(kalpha, 1.0);
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gemm->f_(kbeta, 1.0);
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gemm->i_(ktransA, 0);
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gemm->i_(ktransB, 0);
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gemm->insertBefore(orig_matmul->node());
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n->replaceAllUsesWith(gemm);
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destroy_current = NodeDestroyType::DestroyTwo;
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return true;
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}
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};
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} // namespace optimization
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} // namespace ONNX_NAMESPACE

‎onnx/test/optimizer_test.py

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@@ -653,6 +653,116 @@ def test_fuse_add_bias_into_conv_squeeze_4d_bias_no_fuse(self): # type: () -> N
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assert optimized_model.graph.node[0].op_type == 'Conv'
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assert optimized_model.graph.node[1].op_type == 'Add'
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def test_fuse_matmul_add_bias_into_gemm(self): # type: () -> None
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matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
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add = helper.make_node("Add", ["Z", "B"], ["A"])
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graph = helper.make_graph(
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[matmul, add],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (16,))],
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[helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
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)
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optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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assert len(list(optimized_model.graph.node)) == 1
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assert optimized_model.graph.node[0].op_type == "Gemm"
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def test_fuse_matmul_add_bias_into_gemm_2d_bias(self): # type: () -> None
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matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
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add = helper.make_node("Add", ["Z", "B"], ["A"])
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graph = helper.make_graph(
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[matmul, add],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16))],
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[helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
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)
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optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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assert len(list(optimized_model.graph.node)) == 1
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assert optimized_model.graph.node[0].op_type == "Gemm"
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def test_fuse_matmul_add_bias_into_gemm_2d_bias_same_shape(self): # type: () -> None
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matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
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add = helper.make_node("Add", ["Z", "B"], ["A"])
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graph = helper.make_graph(
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[matmul, add],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (32, 16))],
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[helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
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)
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optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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assert len(list(optimized_model.graph.node)) == 1
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assert optimized_model.graph.node[0].op_type == "Gemm"
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def test_fuse_matmul_add_bias_into_gemm_2d_bias_bcast_no_fuse(self): # type: () -> None
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matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
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add = helper.make_node("Add", ["Z", "B"], ["A"])
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graph = helper.make_graph(
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[matmul, add],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (16, 16))],
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[helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 16))]
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)
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optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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assert optimized_model.graph == graph
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def test_fuse_matmul_add_bias_into_gemm_3d_matmul_no_fuse(self): # type: () -> None
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matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
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add = helper.make_node("Add", ["Z", "B"], ["A"])
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graph = helper.make_graph(
723+
[matmul, add],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4, 3)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (3, 3))],
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[helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 3))]
729+
)
730+
optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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assert optimized_model.graph == graph
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def test_fuse_matmul_add_bias_into_gemm_3d_bias_no_fuse(self): # type: () -> None
735+
matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
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add = helper.make_node("Add", ["Z", "B"], ["A"])
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graph = helper.make_graph(
738+
[matmul, add],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 1, 16))],
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[helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))]
744+
)
745+
optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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747+
assert optimized_model.graph == graph
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def test_fuse_matmul_add_bias_into_gemm_multiple_use_no_fuse(self): # type: () -> None
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matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"])
751+
identity = helper.make_node("Identity", ["Z"], ["A1"])
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add = helper.make_node("Add", ["Z", "B"], ["A2"])
753+
graph = helper.make_graph(
754+
[matmul, add, identity],
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"test",
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[helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)),
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)),
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helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16))],
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[helper.make_tensor_value_info("A1", TensorProto.FLOAT, (32, 16)),
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helper.make_tensor_value_info("A2", TensorProto.FLOAT, (32, 16))]
761+
)
762+
optimized_model = self._optimized(graph, ["fuse_matmul_add_bias_into_gemm"])
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764+
assert optimized_model.graph == graph
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def test_fuse_pad_into_conv(self): # type: () -> None
657767
pad = helper.make_node(
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"Pad",

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