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https://github.com/saymrwulf/onnxruntime.git
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Fix bug where linear_output_ is not cleared when linear_before_reset is true and no bias input is provided. Requires a batch size of 3 or more to trigger if initial_h is not provided. (#3893)
This commit is contained in:
Scott McKay
GitHub
parent
475ea382f9
commit
99415f09fe
3 changed files with 47 additions and 35 deletions
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@ -526,22 +526,18 @@ void UniDirectionalGru<T>::Compute(const gsl::span<const T>& inputs_arg,
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const int total_rows = max_sequence_length * batch_size_;
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float alpha = 1.0f;
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float beta = 0.0f; // zero out outputZRH_ when calling ComputeGemm.
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// apply weights to all the inputs
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ComputeGemm(total_rows, hidden_size_x3, input_size_, alpha,
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inputs.cbegin(), inputs.cend(),
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input_size_,
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input_weights.cbegin(), input_weights.cend(),
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input_size_, beta,
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input_size_, 0.f,
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outputZRH_.begin(), outputZRH_.end(),
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hidden_size_x3, ttp_);
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DumpMatrix("inputs with weights applied", outputZRH_.data(), seq_length_ * batch_size_ * 3, hidden_size_);
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// set to 1 so the weighted inputs in outputZRH_ are added to the result in the next call to ComputeGemm
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beta = 1.0f;
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// output shape is [seq_length, num_directions, batch_size, hidden_size]
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// if we are doing 2 directions and this is the forward pass we're writing to the real output so
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// need to include num_directions in the step length.
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@ -599,7 +595,7 @@ void UniDirectionalGru<T>::Compute(const gsl::span<const T>& inputs_arg,
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prev_Ht, prev_Ht_end,
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hidden_size_,
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recurrent_weightsZR.cbegin(), recurrent_weightsZR.cend(),
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hidden_size_, beta,
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hidden_size_, 1.f, // beta == 1 so we add existing values in outputZRH_
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outputZRH_.begin() + out_added_offset, outputZRH_.end(),
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hidden_size_x3, ttp_);
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@ -619,8 +615,10 @@ void UniDirectionalGru<T>::Compute(const gsl::span<const T>& inputs_arg,
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prev_Ht, prev_Ht_end, // Ht-1
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hidden_size_,
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recurrent_weightsH.cbegin(), recurrent_weightsH.cend(), // Rh^T
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hidden_size_, beta,
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linear_output_.begin(), linear_output_.end(), // pre: Rbh if use_bias_, post:output
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hidden_size_,
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use_bias_ ? 1.f : 0.f, // don't add values in linear_output_ if no bias input
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linear_output_.begin(),
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linear_output_.end(), // pre: Rbh if use_bias_, post:output
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hidden_size_, ttp_);
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DumpMatrix("Ht-1 * (Rh^T) + Rbh " + seqno_str, linear_output_.data(), batch_size_, hidden_size_);
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@ -690,7 +688,7 @@ void UniDirectionalGru<T>::Compute(const gsl::span<const T>& inputs_arg,
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cur_h_local, cur_h_local_end, // rt (.) Ht-1
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hidden_size_,
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recurrent_weightsH.cbegin(), recurrent_weightsH.cend(), // Rh^T
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hidden_size_, beta,
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hidden_size_, 1.f, // beta == 1 to add Xt*(Wh^T) from out_H
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out_H, outputZRH_.end(),
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hidden_size_x3, ttp_);
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}
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@ -830,9 +828,7 @@ void UniDirectionalGru<T>::AllocateBuffers() {
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}
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if (linear_before_reset_) {
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// if use_bias_ is true we copy bias values to this as the first use. if it's false we don't and need to initialize
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bool fill = !use_bias_;
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linear_output_ = Allocate(allocator_, batch_size_ * hidden_size_, linear_output_ptr_, fill);
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linear_output_ = Allocate(allocator_, batch_size_ * hidden_size_, linear_output_ptr_);
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}
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auto batch_times_seq_length = batch_size_ * seq_length_;
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@ -260,17 +260,20 @@ class GRU_ONNXRuntimeUnitTests():
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print_results(fw_output)
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@staticmethod
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def BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRows(linear_before_reset=0):
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def BidirectionalDefaultActivationsSimpleWeightsNoBias(linear_before_reset=0):
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print(GRU_ONNXRuntimeUnitTests.BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRows.__name__ +
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print(GRU_ONNXRuntimeUnitTests.BidirectionalDefaultActivationsSimpleWeightsNoBias.__name__ +
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'.linear_before_reset=' + str(linear_before_reset))
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seq_length = 2
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batch_size = 2
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batch_size = 3 if linear_before_reset else 2
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input_size = 1
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hidden_size = 3
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input = np.array([[[1.], [2.]], [[10.], [11.]]]).astype(np.float32)
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if linear_before_reset:
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input = np.array([[[1.], [2.], [3.]], [[10.], [11.], [12.]]]).astype(np.float32)
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else:
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input = np.array([[[1.], [2.]], [[10.], [11.]]]).astype(np.float32)
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W = np.array([0.1, 0.2, 0.3, 1, 2, 3, 10, 11, 12]).astype(np.float32).reshape(1, 3 * hidden_size, input_size)
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@ -384,8 +387,8 @@ class GRU_ONNXRuntimeUnitTests():
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GRU_ONNXRuntimeUnitTests.ForwardDefaultActivationsSimpleWeightsNoBiasTwoRows()
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GRU_ONNXRuntimeUnitTests.ReverseDefaultActivationsSimpleWeightsNoBiasTwoRows()
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GRU_ONNXRuntimeUnitTests.BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRows()
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GRU_ONNXRuntimeUnitTests.BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRows(linear_before_reset=1)
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GRU_ONNXRuntimeUnitTests.BidirectionalDefaultActivationsSimpleWeightsNoBias()
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GRU_ONNXRuntimeUnitTests.BidirectionalDefaultActivationsSimpleWeightsNoBias(linear_before_reset=1)
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GRU_ONNXRuntimeUnitTests.ForwardDefaultActivationsSimpleWeightsWithBiasBatchParallel()
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GRU_ONNXRuntimeUnitTests.ForwardDefaultActivationsSimpleWeightsWithBiasBatchParallelLinearBeforeReset()
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@ -106,14 +106,21 @@ void DefaultActivationsSimpleWeightsNoBias(std::string direction,
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const std::vector<float>& Y_h_data,
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bool linear_before_reset = false) {
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int64_t seq_length = 2;
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int batch_size = 2;
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int batch_size = linear_before_reset ? 3 : 2; // extra row to validate usage of linear_output_
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int64_t input_size = 1;
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int64_t hidden_size = 3;
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int num_directions = direction == "bidirectional" ? 2 : 1;
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std::vector<float> X_data{1.f, 2.f,
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10.f, 11.f};
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std::vector<float> X_data;
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if (linear_before_reset) {
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X_data = {1.f, 2.f, 3.f,
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10.f, 11.f, 12.f};
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} else {
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X_data = {1.f, 2.f,
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10.f, 11.f};
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}
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std::vector<float> W_data{0.1f, 0.2f, 0.3f, // wz
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1.f, 2.f, 3.f, // wr
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@ -170,7 +177,7 @@ TEST(GRUTest, ReverseDefaultActivationsSimpleWeightsNoBiasTwoRows) {
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DefaultActivationsSimpleWeightsNoBias("reverse", Y_data, Y_h_data);
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}
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TEST(GRUTest, BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRows) {
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TEST(GRUTest, BidirectionalDefaultActivationsSimpleWeightsNoBias) {
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std::vector<float> Y_data{
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// forward output for input sequence 0
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0.4750208f, 0.450166f, 0.4255575f,
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@ -200,32 +207,38 @@ TEST(GRUTest, BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRows) {
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DefaultActivationsSimpleWeightsNoBias("bidirectional", Y_data, Y_h_data);
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}
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TEST(GRUTest, BidirectionalDefaultActivationsSimpleWeightsNoBiasTwoRowsLinearBeforeReset) {
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TEST(GRUTest, BidirectionalDefaultActivationsSimpleWeightsNoBiasLinearBeforeReset) {
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std::vector<float> Y_data{
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// forward output for input sequence 0
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0.47502081f, 0.450166f, 0.42555748f,
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0.450166f, 0.40131234f, 0.35434369f,
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0.4750208f, 0.450166f, 0.4255575f,
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0.45016602f, 0.40131235f, 0.35434368f,
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0.42555748f, 0.35434369f, 0.28905049f,
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// reverse output for input sequence 0 [sequence 1 in reversed input]
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0.60827853f, 0.50623393f, 0.4426924f,
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0.5803454f, 0.4527356f, 0.36886264f,
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0.6082785f, 0.50623393f, 0.4426924f,
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0.5803454f, 0.4527356f, 0.36886263f,
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0.5521325f, 0.40092295f, 0.30118297f,
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// forward output for input sequence 1
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0.60270932f, 0.50830227f, 0.44950222f,
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0.57543688f, 0.45485455f, 0.37478411f,
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0.6027093f, 0.5083023f, 0.44950223f,
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0.5754369f, 0.45485455f, 0.3747841f,
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0.54791767f, 0.40301081f, 0.30608854f,
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// reverse output for input sequence 1 [sequence 0 in reversed input]
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0.26894142f, 0.11920292f, 0.04742587f,
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0.24973989f, 0.09975048f, 0.03557118f};
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0.26894143f, 0.11920292f, 0.04742587f,
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0.24973989f, 0.09975048f, 0.03557118f,
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0.23147521f, 0.08317269f, 0.02659699f};
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std::vector<float> Y_h_data{
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// we did the forward processing of input[1] last
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0.60270932f, 0.50830227f, 0.44950222f,
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0.57543688f, 0.45485455f, 0.37478411f,
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0.6027093f, 0.5083023f, 0.44950223f,
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0.5754369f, 0.45485455f, 0.3747841f,
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0.54791767f, 0.40301081f, 0.30608854f,
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// and the reverse processing of input[0] last as the input order was reversed
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0.60827853f, 0.50623393f, 0.4426924f,
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0.5803454f, 0.4527356f, 0.36886264f};
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0.6082785f, 0.50623393f, 0.4426924f,
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0.5803454f, 0.4527356f, 0.36886263f,
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0.5521325f, 0.40092295f, 0.30118297f};
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DefaultActivationsSimpleWeightsNoBias("bidirectional", Y_data, Y_h_data, true);
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}
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