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Quantized LSTM/GRU: Remove legacy API support (#72522)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72522 Ref #72263 for cpp_custom_type_hack removal These overloads were deprecated in #35787 which was in the PyTorch 1.6 release, so the BC period is well expired. cc jamesr66a Test Plan: Imported from OSS Reviewed By: bdhirsh Differential Revision: D34111271 Pulled By: albanD fbshipit-source-id: 0078564188133625ca67137975fd5dd2fa2b4827
This commit is contained in:
Peter Bell
Facebook GitHub Bot
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commit
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@ -3,7 +3,6 @@
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#include <ATen/ATen.h>
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#include <ATen/NativeFunctions.h>
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#include <ATen/core/op_registration/op_registration.h>
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#include <ATen/cpp_custom_type_hack.h>
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#include <ATen/native/quantized/cpu/packed_params.h>
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#include <ATen/native/quantized/cpu/fbgemm_utils.h>
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#include <ATen/native/quantized/cpu/qnnpack_utils.h>
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@ -579,88 +578,6 @@ static std::vector<CellParams> gather_params(TensorList params, bool has_biases,
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return result;
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}
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// These gather_* functions are kept solely for the purposes of backward
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// compatbility in the legacy quantized_{lstm,gru} APIs
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static c10::List<c10::intrusive_ptr<CellParamsBase>> gather_quantized_params(
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c10::List<at::Tensor> params) {
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static at::Tensor undefined;
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std::vector<c10::intrusive_ptr<CellParamsBase>> result;
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TORCH_CHECK(params.size() % 12 == 0, "got an incorrect number of quantized RNN parameters");
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for (size_t i = 0; i < params.size(); i += 12) {
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result.emplace_back(c10::make_intrusive<QuantizedCellParams>(
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static_cast<at::Tensor>(params[i]),
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static_cast<at::Tensor>(params[i + 1]),
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static_cast<at::Tensor>(params[i + 2]),
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static_cast<at::Tensor>(params[i + 3]),
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static_cast<at::Tensor>(params[i + 4]),
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static_cast<at::Tensor>(params[i + 5]),
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static_cast<at::Tensor>(params[i + 6]),
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static_cast<at::Tensor>(params[i + 7]),
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static_cast<at::Tensor>(params[i + 8]).item(),
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static_cast<at::Tensor>(params[i + 9]).item(),
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static_cast<at::Tensor>(params[i + 10]).item(),
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static_cast<at::Tensor>(params[i + 11]).item()));
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}
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return c10::List<c10::intrusive_ptr<CellParamsBase>>(result);
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}
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static c10::List<c10::intrusive_ptr<CellParamsBase>>
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gather_quantized_params_dynamic(c10::List<at::Tensor> params) {
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static at::Tensor undefined;
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std::vector<c10::intrusive_ptr<CellParamsBase>> result;
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for (size_t i = 0; i < params.size(); i += 2) {
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auto packed_struct_ih =
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cpp_custom_type_hack::cast<c10::intrusive_ptr<LinearPackedParamsBase>>(
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static_cast<at::Tensor>(params[i]));
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auto packed_struct_hh =
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cpp_custom_type_hack::cast<c10::intrusive_ptr<LinearPackedParamsBase>>(
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static_cast<at::Tensor>(params[i + 1]));
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auto bias_ih = packed_struct_ih->bias().value_or(undefined);
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auto bias_hh = packed_struct_hh->bias().value_or(undefined);
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result.emplace_back(c10::make_intrusive<QuantizedCellParamsDynamic>(
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std::move(packed_struct_ih),
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std::move(packed_struct_hh),
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std::move(bias_ih),
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std::move(bias_hh)));
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}
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return c10::List<c10::intrusive_ptr<CellParamsBase>>(result);
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}
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static c10::List<c10::intrusive_ptr<CellParamsBase>>
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gather_quantized_params_fp16(c10::List<at::Tensor> params) {
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static at::Tensor undefined;
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std::vector<c10::intrusive_ptr<CellParamsBase>> result;
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TORCH_CHECK(params.size() % 4 == 0,
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"incorrect number of quantized RNN parameters FP16");
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for (size_t i = 0; i < params.size(); i += 4) {
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c10::intrusive_ptr<LinearPackedParamsBase> packed_struct_ih =
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cpp_custom_type_hack::cast<c10::intrusive_ptr<LinearPackedParamsBase>>(
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static_cast<at::Tensor>(params[i]));
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c10::intrusive_ptr<LinearPackedParamsBase> packed_struct_hh =
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cpp_custom_type_hack::cast<c10::intrusive_ptr<LinearPackedParamsBase>>(
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static_cast<at::Tensor>(params[i + 1]));
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// NB: we install the bias from the gathered parameters here because
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// in the "new world", the fp16 linear apply() method always expects
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// the bias to be present in the packed struct. In the "old world",
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// we called `fbgemm_linear_fp16_weight_fp32_activation`, which took
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// the bias explicitly and ignored the bias in the packed struct. To
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// reconcile serialized models that behavied in the old style, we
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// put the bias into the appropriate packed structures here.
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//
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// Hopefully we can remove this in the future when we eliminate
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// the old style altogether
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packed_struct_ih->set_bias(params[i + 2]);
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packed_struct_hh->set_bias(params[i + 3]);
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result.emplace_back(c10::make_intrusive<QuantizedCellParamsFP16>(
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std::move(packed_struct_ih), std::move(packed_struct_hh)));
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}
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return c10::List<c10::intrusive_ptr<CellParamsBase>>(result);
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}
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////////////////////////////////////////////////////////////////////////////////
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// HIDDEN STATE FUNCTIONS
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//
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@ -1411,21 +1328,11 @@ std::tuple<Tensor, Tensor> quantized_gru_input_legacy(
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bool train,
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bool bidirectional,
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bool batch_first) {
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TORCH_WARN_ONCE(
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TORCH_CHECK(
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false,
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"torch.quantized_gru with List[Tensor] for parameters is "
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"deprecated and may be removed! Please re-export your model "
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"no longer supported. Please re-export your model "
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"using the newer definitions in torch.jit.quantized");
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auto params = gather_quantized_params(std::move(_params));
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return quantized_gru_input(
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_input,
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hx,
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std::move(params),
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has_biases,
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num_layers,
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dropout_p,
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train,
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bidirectional,
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batch_first);
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}
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std::tuple<Tensor, Tensor> quantized_gru_data_legacy(
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@ -1438,21 +1345,11 @@ std::tuple<Tensor, Tensor> quantized_gru_data_legacy(
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double dropout_p,
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bool train,
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bool bidirectional) {
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TORCH_WARN_ONCE(
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TORCH_CHECK(
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false,
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"torch.quantized_gru with List[Tensor] for parameters is "
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"deprecated and may be removed! Please re-export your model "
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"no longer supported. Please re-export your model "
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"using the newer definitions in torch.jit.quantized");
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auto params = gather_quantized_params(std::move(_params));
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return quantized_gru_data(
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data,
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batch_sizes,
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hx,
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std::move(params),
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has_biases,
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num_layers,
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dropout_p,
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train,
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bidirectional);
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}
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using tanf_cell_type = SimpleCell<tanh_f, CellParams>;
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@ -1768,34 +1665,11 @@ std::tuple<Tensor, Tensor, Tensor> quantized_lstm_input_legacy(
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bool batch_first,
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c10::optional<ScalarType> dtype,
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bool use_dynamic) {
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TORCH_WARN_ONCE(
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TORCH_CHECK(
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false,
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"torch.quantized_lstm with List[Tensor] for parameters is "
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"deprecated and may be removed! Please re-export your model "
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"no longer supported. Please re-export your model "
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"using the newer definitions in torch.jit.quantized");
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c10::List<c10::intrusive_ptr<CellParamsBase>> params;
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auto result_dtype = dtype.has_value() ? dtype.value() : at::kChar;
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if (result_dtype == at::kChar || result_dtype == at::kQInt8) {
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if (use_dynamic) {
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params = gather_quantized_params_dynamic(std::move(_params_));
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} else {
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params = gather_quantized_params(std::move(_params_));
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}
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} else {
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params = gather_quantized_params_fp16(std::move(_params_));
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}
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return quantized_lstm_input(
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_input,
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std::move(hx_),
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std::move(params),
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has_biases,
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num_layers,
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dropout_p,
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train,
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bidirectional,
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batch_first,
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// NOLINTNEXTLINE(performance-move-const-arg)
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std::move(dtype),
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use_dynamic);
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}
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std::tuple<Tensor, Tensor, Tensor> quantized_lstm_data(
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@ -1857,34 +1731,11 @@ std::tuple<Tensor, Tensor, Tensor> quantized_lstm_data_legacy(
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bool bidirectional,
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c10::optional<ScalarType> dtype,
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bool use_dynamic) {
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TORCH_WARN_ONCE(
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TORCH_CHECK(
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false,
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"torch.quantized_lstm with List[Tensor] for parameters is "
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"deprecated and may be removed! Please re-export your model "
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"no longer supported. Please re-export your model "
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"using the newer definitions in torch.jit.quantized");
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c10::List<c10::intrusive_ptr<CellParamsBase>> params;
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auto result_dtype = dtype.has_value() ? dtype.value() : at::kChar;
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if (result_dtype == at::kChar || result_dtype == at::kQInt8) {
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if (use_dynamic) {
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params = gather_quantized_params_dynamic(std::move(_params_));
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} else {
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params = gather_quantized_params(std::move(_params_));
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}
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} else {
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params = gather_quantized_params_fp16(std::move(_params_));
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}
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return quantized_lstm_data(
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data,
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batch_sizes,
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std::move(hx_),
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std::move(params),
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has_biases,
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num_layers,
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dropout_p,
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train,
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bidirectional,
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// NOLINTNEXTLINE(performance-move-const-arg)
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std::move(dtype),
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use_dynamic);
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}
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#define DEFINE_QUANTIZED_RNN_CELL(name, hx_type, cell_type, return_type, prepare_hx_fn) \
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