mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
3bf922a6ce
Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/106249 Approved by: https://github.com/Skylion007
799 lines
27 KiB
Python
799 lines
27 KiB
Python
import warnings
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from typing import List, Optional, Tuple
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import torch
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from torch import _VF, Tensor # noqa: F401
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from torch.nn.utils.rnn import PackedSequence
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class QuantizedLinear(torch.jit.ScriptModule):
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__constants__ = ["scale", "zero_point"]
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def __init__(self, other):
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super().__init__()
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warnings.warn(
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"torch.jit.QuantizedLinear is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.Linear instead."
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)
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self.in_features = other.in_features
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self.out_features = other.out_features
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# Quantize weight and discard the original
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(
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self.weight,
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self.col_offsets,
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self.scale,
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self.zero_point,
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) = torch.fbgemm_linear_quantize_weight(
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other.weight.clone(memory_format=torch.contiguous_format).float()
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)
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self.weight = torch.nn.Parameter(self.weight, requires_grad=False)
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self.col_offsets = torch.nn.Parameter(self.col_offsets, requires_grad=False)
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assert other.bias is not None, "QuantizedLinear requires a bias"
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self.bias = torch.nn.Parameter(
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other.bias.clone(memory_format=torch.contiguous_format).float(),
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requires_grad=False,
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)
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self.register_buffer(
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"packed_tensor_ptr",
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torch.fbgemm_pack_quantized_matrix(
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self.weight.clone(memory_format=torch.contiguous_format)
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),
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)
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@torch.jit.script_method
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def _unpack(self):
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self.packed_tensor_ptr.set_(torch.fbgemm_pack_quantized_matrix(self.weight))
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@torch.jit.script_method
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def _pack(self):
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self.packed_tensor_ptr.set_(
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torch.zeros(torch.jit.annotate(List[int], []), dtype=torch.uint8).detach()
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)
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@torch.jit.script_method
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def forward(self, input):
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out = torch.fbgemm_linear_int8_weight_fp32_activation(
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input.float(),
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self.weight,
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self.packed_tensor_ptr,
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self.col_offsets,
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self.scale,
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self.zero_point,
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self.bias,
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)
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return out.to(input.dtype)
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def extra_repr(self):
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repr = (
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"in_features={in_features}, out_features={out_features}, "
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"scale={scale}, zero_point={zero_point}".format(**self.__dict__)
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)
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return repr
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# FP16 weights
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class QuantizedLinearFP16(torch.jit.ScriptModule):
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def __init__(self, other):
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super().__init__()
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warnings.warn(
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"torch.jit.QuantizedLinearFP16 is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.Linear instead."
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)
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self.in_features = other.in_features
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self.out_features = other.out_features
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self.original_weight = other.weight
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self.weight = torch.fbgemm_pack_gemm_matrix_fp16(
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other.weight.clone(memory_format=torch.contiguous_format).float()
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)
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assert other.bias is not None, "QuantizedLinearFP16 requires a bias"
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self.bias = torch.nn.Parameter(
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other.bias.clone(memory_format=torch.contiguous_format).float(),
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requires_grad=False,
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)
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self.register_buffer("packed_weight", self.weight)
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@torch.jit.script_method
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def _unpack(self):
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self.packed_weight.set_(
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torch.fbgemm_pack_gemm_matrix_fp16(self.original_weight)
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)
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@torch.jit.script_method
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def _pack(self):
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self.packed_weight.set_(
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torch.zeros(torch.jit.annotate(List[int], []), dtype=torch.uint8).detach()
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)
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@torch.jit.script_method
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def forward(self, input):
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out = torch.fbgemm_linear_fp16_weight_fp32_activation(
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input.float(), self.packed_weight, self.bias
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)
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return out
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def extra_repr(self):
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repr = "in_features={in_features}, out_features={out_features}, ".format(
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**self.__dict__
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)
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return repr
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# Quantized RNN cell implementations
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class QuantizedRNNCellBase(torch.jit.ScriptModule):
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__constants__ = [
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"input_size",
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"hidden_size",
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"bias",
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"scale_hh",
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"scale_ih",
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"zero_point_ih",
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"zero_point_hh",
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]
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def __init__(self, other):
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super().__init__()
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warnings.warn(
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"torch.jit.QuantizedRNNCellBase is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.RNNCell instead."
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)
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self.input_size = other.input_size
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self.hidden_size = other.hidden_size
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self.bias = other.bias
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if not self.bias:
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raise ValueError("Quantized RNN cells require bias terms")
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(
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weight_ih,
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col_offsets_ih,
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self.scale_ih,
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self.zero_point_ih,
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) = torch.fbgemm_linear_quantize_weight(
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other.weight_ih.clone(memory_format=torch.contiguous_format).float()
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)
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self.register_buffer("weight_ih", weight_ih)
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self.register_buffer("col_offsets_ih", col_offsets_ih)
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(
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weight_hh,
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col_offsets_hh,
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self.scale_hh,
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self.zero_point_hh,
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) = torch.fbgemm_linear_quantize_weight(
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other.weight_hh.clone(memory_format=torch.contiguous_format).float()
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)
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self.register_buffer("weight_hh", weight_hh)
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self.register_buffer("col_offsets_hh", col_offsets_hh)
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packed_ih = torch.fbgemm_pack_quantized_matrix(self.weight_ih)
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self.register_buffer("packed_ih", packed_ih)
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packed_hh = torch.fbgemm_pack_quantized_matrix(self.weight_hh)
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self.register_buffer("packed_hh", packed_hh)
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self.bias_ih = torch.nn.Parameter(
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other.bias_ih.clone(memory_format=torch.contiguous_format).float(),
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requires_grad=False,
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)
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self.bias_hh = torch.nn.Parameter(
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other.bias_hh.clone(memory_format=torch.contiguous_format).float(),
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requires_grad=False,
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)
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def extra_repr(self):
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s = "{input_size}, {hidden_size}"
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if "bias" in self.__dict__ and self.bias is not True:
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s += ", bias={bias}"
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if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
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s += ", nonlinearity={nonlinearity}"
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return s.format(**self.__dict__)
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@torch.jit.script_method
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def check_forward_input(self, input):
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if input.size(1) != self.input_size:
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raise RuntimeError(
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f"input has inconsistent input_size: got {input.size(1)}, expected {self.input_size}"
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)
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@torch.jit.script_method
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def check_forward_hidden(
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self, input: Tensor, hx: Tensor, hidden_label: str = ""
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) -> None:
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if input.size(0) != hx.size(0):
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raise RuntimeError(
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f"Input batch size {input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
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)
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if hx.size(1) != self.hidden_size:
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raise RuntimeError(
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f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
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)
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# TODO: for some reason weak_script_method causes a destruction of the
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# module to occur, which in turn frees the packed_ih object via its DataPtr
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# deleter. This is bizarre and should probably get fixed.
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# @torch._jit_internal.weak_script_method
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@torch.jit.script_method
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def _unpack(self):
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self.packed_ih.set_(torch.fbgemm_pack_quantized_matrix(self.weight_ih))
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self.packed_hh.set_(torch.fbgemm_pack_quantized_matrix(self.weight_hh))
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# @torch._jit_internal.weak_script_method
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@torch.jit.script_method
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def _pack(self):
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self.packed_ih.set_(
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torch.zeros(torch.jit.annotate(List[int], []), dtype=torch.uint8).detach()
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)
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self.packed_hh.set_(
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torch.zeros(torch.jit.annotate(List[int], []), dtype=torch.uint8).detach()
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)
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class QuantizedRNNCell(QuantizedRNNCellBase):
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__constants__ = [
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"input_size",
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"hidden_size",
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"bias",
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"scale_hh",
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"scale_ih",
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"zero_point_ih",
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"zero_point_hh",
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"nonlinearity",
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]
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def __init__(self, other):
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super().__init__(other)
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warnings.warn(
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"torch.jit.QuantizedRNNCell is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.RNNCell instead."
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)
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self.nonlinearity = other.nonlinearity
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@torch.jit.script_method
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def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
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self.check_forward_input(input)
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if hx is None:
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hx = torch.zeros(
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input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
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)
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self.check_forward_hidden(input, hx, "")
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if self.nonlinearity == "tanh":
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ret = _VF.quantized_rnn_tanh_cell(
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input,
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hx,
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self.weight_ih,
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self.weight_hh,
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self.bias_ih,
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self.bias_hh,
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self.packed_ih,
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self.packed_hh,
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self.col_offsets_ih,
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self.col_offsets_hh,
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self.scale_ih,
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self.scale_hh,
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self.zero_point_ih,
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self.zero_point_hh,
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)
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elif self.nonlinearity == "relu":
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ret = _VF.quantized_rnn_relu_cell(
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input,
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hx,
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self.weight_ih,
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self.weight_hh,
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self.bias_ih,
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self.bias_hh,
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self.packed_ih,
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self.packed_hh,
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self.col_offsets_ih,
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self.col_offsets_hh,
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self.scale_ih,
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self.scale_hh,
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self.zero_point_ih,
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self.zero_point_hh,
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)
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else:
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ret = input # TODO: remove when jit supports exception flow
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raise RuntimeError(f"Unknown nonlinearity: {self.nonlinearity}")
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return ret
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class QuantizedLSTMCell(QuantizedRNNCellBase):
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def __init__(self, other):
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super().__init__(other)
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warnings.warn(
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"torch.jit.QuantizedLSTMCell is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.LSTMCell instead."
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)
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@torch.jit.script_method
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def forward(
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self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
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) -> Tuple[Tensor, Tensor]:
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self.check_forward_input(input)
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if hx is None:
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zeros = torch.zeros(
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input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
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)
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hx = (zeros, zeros)
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self.check_forward_hidden(input, hx[0], "[0]")
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self.check_forward_hidden(input, hx[1], "[1]")
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return _VF.quantized_lstm_cell(
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input,
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hx,
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self.weight_ih,
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self.weight_hh,
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self.bias_ih,
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self.bias_hh,
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self.packed_ih,
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self.packed_hh,
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self.col_offsets_ih,
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self.col_offsets_hh,
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self.scale_ih,
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self.scale_hh,
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self.zero_point_ih,
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self.zero_point_hh,
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)
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class QuantizedGRUCell(QuantizedRNNCellBase):
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def __init__(self, other):
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super().__init__(other)
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warnings.warn(
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"torch.jit.QuantizedGRUCell is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.GRUCell instead."
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)
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@torch.jit.script_method
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def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
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self.check_forward_input(input)
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if hx is None:
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hx = torch.zeros(
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input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
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)
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self.check_forward_hidden(input, hx, "")
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return _VF.quantized_gru_cell(
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input,
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hx,
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self.weight_ih,
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self.weight_hh,
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self.bias_ih,
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self.bias_hh,
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self.packed_ih,
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self.packed_hh,
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self.col_offsets_ih,
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self.col_offsets_hh,
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self.scale_ih,
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self.scale_hh,
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self.zero_point_ih,
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self.zero_point_hh,
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)
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def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
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return tensor.index_select(dim, permutation)
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class QuantizedRNNBase(torch.jit.ScriptModule):
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__constants__ = [
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"mode",
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"input_size",
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"hidden_size",
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"num_layers",
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"bias",
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"batch_first",
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"dropout",
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"bidirectional",
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"dtype",
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]
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def __init__(self, other, dtype=torch.int8):
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super().__init__()
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warnings.warn(
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"torch.jit.QuantizedRNNBase is deprecated and will be removed in an upcoming "
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"PyTorch release. Please use the torch.ao.nn.quantized.dynamic instead."
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)
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self.mode = other.mode
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self.input_size = other.input_size
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self.hidden_size = other.hidden_size
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self.num_layers = other.num_layers
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self.bias = other.bias
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self.batch_first = other.batch_first
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if self.mode != "GRU":
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assert not self.batch_first
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self.dropout = other.dropout
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self.bidirectional = other.bidirectional
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num_directions = 2 if self.bidirectional else 1
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self.dtype = dtype
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assert self.bias
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# TODO: support more than just LSTM
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if self.mode != "LSTM" and self.mode != "GRU":
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raise RuntimeError("Only LSTM or GRU is supported for QuantizedRNN")
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if dtype != torch.int8 and dtype != torch.float16:
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raise RuntimeError(f"Unsupported dtype: {dtype}")
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self.all_weights = []
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for layer in range(self.num_layers):
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for direction in range(num_directions):
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layer_input_size = (
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self.input_size if layer == 0 else self.hidden_size * num_directions
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)
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suffix = "_reverse" if direction == 1 else ""
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def get_weight_bias(ihhh):
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weight_name = f"weight_{ihhh}_l{layer}{suffix}"
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bias_name = f"bias_{ihhh}_l{layer}{suffix}"
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weight = getattr(other, weight_name)
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bias = getattr(other, bias_name)
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return weight, bias
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weight_ih, bias_ih = get_weight_bias("ih")
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weight_hh, bias_hh = get_weight_bias("hh")
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if dtype == torch.int8:
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cell_params = torch.ops.quantized.make_quantized_cell_params(
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weight_ih, weight_hh, bias_ih, bias_hh
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)
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else:
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packed_ih = torch.ops.quantized.linear_prepack_fp16(
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weight_ih.float(), bias_ih
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)
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packed_hh = torch.ops.quantized.linear_prepack_fp16(
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weight_hh.float(), bias_hh
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)
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cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
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packed_ih, packed_hh
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)
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setattr(self, f"cell_params_{layer}_{suffix}", cell_params)
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self.all_weights.append(cell_params)
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|
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@torch.jit.script_method
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def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
|
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expected_input_dim = 2 if batch_sizes is not None else 3
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if input.dim() != expected_input_dim:
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raise RuntimeError(
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f"input must have {expected_input_dim} dimensions, got {input.dim()}"
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)
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if self.input_size != input.size(-1):
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raise RuntimeError(
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f"input.size(-1) must be equal to input_size. Expected {self.input_size}, got {input.size(-1)}"
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)
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@torch.jit.script_method
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def get_expected_hidden_size(
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self, input: Tensor, batch_sizes: Optional[Tensor]
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) -> Tuple[int, int, int]:
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if batch_sizes is not None:
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mini_batch = int(batch_sizes[0])
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else:
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mini_batch = input.size(0) if self.batch_first else input.size(1)
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num_directions = 2 if self.bidirectional else 1
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expected_hidden_size = (
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self.num_layers * num_directions,
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mini_batch,
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self.hidden_size,
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)
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return expected_hidden_size
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|
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@torch.jit.script_method
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def check_hidden_size(
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self,
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hx: Tensor,
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expected_hidden_size: Tuple[int, int, int],
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msg: str = "Expected hidden size {}, got {}",
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) -> None:
|
|
if hx.size() != expected_hidden_size:
|
|
raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))
|
|
|
|
@torch.jit.script_method
|
|
def check_forward_args(
|
|
self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
|
|
) -> None:
|
|
self.check_input(input, batch_sizes)
|
|
expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
|
|
self.check_hidden_size(
|
|
hidden, expected_hidden_size, msg="Expected hidden size {}, got {}"
|
|
)
|
|
|
|
@torch.jit.script_method
|
|
def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
|
|
if permutation is None:
|
|
return hx
|
|
return apply_permutation(hx, permutation)
|
|
|
|
|
|
class QuantizedLSTM(QuantizedRNNBase):
|
|
__overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
|
|
|
|
def __init__(self, other, dtype):
|
|
super().__init__(other, dtype)
|
|
warnings.warn(
|
|
"torch.jit.QuantizedLSTM is deprecated and will be removed in an upcoming "
|
|
"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.LSTM instead."
|
|
)
|
|
|
|
@torch.jit.script_method
|
|
def forward_impl(
|
|
self,
|
|
input: Tensor,
|
|
hx: Optional[Tuple[Tensor, Tensor]],
|
|
batch_sizes: Optional[Tensor],
|
|
max_batch_size: int,
|
|
sorted_indices: Optional[Tensor],
|
|
) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
|
|
if hx is None:
|
|
num_directions = 2 if self.bidirectional else 1
|
|
zeros = torch.zeros(
|
|
self.num_layers * num_directions,
|
|
max_batch_size,
|
|
self.hidden_size,
|
|
dtype=input.dtype,
|
|
device=input.device,
|
|
)
|
|
hx = (zeros, zeros)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
|
|
self.check_forward_args(input, hx, batch_sizes)
|
|
assert batch_sizes is None
|
|
result = torch.quantized_lstm(
|
|
input,
|
|
hx,
|
|
self.all_weights,
|
|
self.bias,
|
|
self.num_layers,
|
|
float(self.dropout),
|
|
self.training,
|
|
self.bidirectional,
|
|
self.batch_first,
|
|
dtype=self.dtype,
|
|
use_dynamic=False,
|
|
)
|
|
output = result[0]
|
|
hidden = result[1:]
|
|
|
|
return output, hidden
|
|
|
|
@torch.jit.script_method
|
|
def forward_tensor(
|
|
self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
|
|
) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
|
|
batch_sizes = None
|
|
max_batch_size = input.size(0) if self.batch_first else input.size(1)
|
|
sorted_indices = None
|
|
unsorted_indices = None
|
|
|
|
output, hidden = self.forward_impl(
|
|
input, hx, batch_sizes, max_batch_size, sorted_indices
|
|
)
|
|
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
@torch.jit.script_method
|
|
def forward_packed(
|
|
self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
|
|
) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]:
|
|
input_, batch_sizes, sorted_indices, unsorted_indices = input
|
|
max_batch_size = int(batch_sizes[0])
|
|
|
|
output, hidden = self.forward_impl(
|
|
input_, hx, batch_sizes, max_batch_size, sorted_indices
|
|
)
|
|
|
|
output = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
@torch.jit.script_method
|
|
def permute_hidden(
|
|
self, hx: Tuple[Tensor, Tensor], permutation: Optional[Tensor]
|
|
) -> Tuple[Tensor, Tensor]:
|
|
if permutation is None:
|
|
return hx
|
|
return apply_permutation(hx[0], permutation), apply_permutation(
|
|
hx[1], permutation
|
|
)
|
|
|
|
@torch.jit.script_method
|
|
def check_forward_args(
|
|
self,
|
|
input: Tensor,
|
|
hidden: Tuple[Tensor, Tensor],
|
|
batch_sizes: Optional[Tensor],
|
|
) -> None:
|
|
self.check_input(input, batch_sizes)
|
|
expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
|
|
|
|
self.check_hidden_size(
|
|
hidden[0], expected_hidden_size, "Expected hidden[0] size {}, got {}"
|
|
)
|
|
self.check_hidden_size(
|
|
hidden[1], expected_hidden_size, "Expected hidden[1] size {}, got {}"
|
|
)
|
|
|
|
def forward(self, input, hx=None):
|
|
if isinstance(input, PackedSequence):
|
|
return self.forward_packed(input, hx)
|
|
else:
|
|
return self.forward_tensor(input, hx)
|
|
|
|
|
|
class QuantizedGRU(QuantizedRNNBase):
|
|
__overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
warnings.warn(
|
|
"torch.jit.QuantizedGRU is deprecated and will be removed in an upcoming "
|
|
"PyTorch release. Please use the torch.ao.nn.quantized.dynamic.GRU instead."
|
|
)
|
|
|
|
@torch.jit.script_method
|
|
def forward_impl(
|
|
self,
|
|
input: Tensor,
|
|
hx: Optional[Tensor],
|
|
batch_sizes: Optional[Tensor],
|
|
max_batch_size: int,
|
|
sorted_indices: Optional[Tensor],
|
|
) -> Tuple[Tensor, Tensor]:
|
|
if hx is None:
|
|
num_directions = 2 if self.bidirectional else 1
|
|
hx = torch.zeros(
|
|
self.num_layers * num_directions,
|
|
max_batch_size,
|
|
self.hidden_size,
|
|
dtype=input.dtype,
|
|
device=input.device,
|
|
)
|
|
else:
|
|
# Each batch of the hidden state should match the input sequence that
|
|
# the user believes he/she is passing in.
|
|
hx = self.permute_hidden(hx, sorted_indices)
|
|
|
|
self.check_forward_args(input, hx, batch_sizes)
|
|
if batch_sizes is None:
|
|
result = torch.quantized_gru(
|
|
input,
|
|
hx,
|
|
self.all_weights,
|
|
self.bias,
|
|
self.num_layers,
|
|
float(self.dropout),
|
|
self.training,
|
|
self.bidirectional,
|
|
self.batch_first,
|
|
)
|
|
else:
|
|
result = torch.quantized_gru(
|
|
input,
|
|
batch_sizes,
|
|
hx,
|
|
self.all_weights,
|
|
self.bias,
|
|
self.num_layers,
|
|
float(self.dropout),
|
|
self.training,
|
|
self.bidirectional,
|
|
)
|
|
|
|
output = result[0]
|
|
hidden = result[1]
|
|
|
|
return output, hidden
|
|
|
|
@torch.jit.script_method
|
|
def forward_tensor(
|
|
self, input: Tensor, hx: Optional[Tensor] = None
|
|
) -> Tuple[Tensor, Tensor]:
|
|
batch_sizes = None
|
|
max_batch_size = input.size(0) if self.batch_first else input.size(1)
|
|
sorted_indices = None
|
|
unsorted_indices = None
|
|
|
|
output, hidden = self.forward_impl(
|
|
input, hx, batch_sizes, max_batch_size, sorted_indices
|
|
)
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
@torch.jit.script_method
|
|
def forward_packed(
|
|
self, input: PackedSequence, hx: Optional[Tensor] = None
|
|
) -> Tuple[PackedSequence, Tensor]:
|
|
input_, batch_sizes, sorted_indices, unsorted_indices = input
|
|
max_batch_size = int(batch_sizes[0])
|
|
|
|
output, hidden = self.forward_impl(
|
|
input_, hx, batch_sizes, max_batch_size, sorted_indices
|
|
)
|
|
|
|
output = PackedSequence(output, batch_sizes, sorted_indices, unsorted_indices)
|
|
return output, self.permute_hidden(hidden, unsorted_indices)
|
|
|
|
def forward(self, input, hx=None):
|
|
if isinstance(input, PackedSequence):
|
|
return self.forward_packed(input, hx)
|
|
else:
|
|
return self.forward_tensor(input, hx)
|
|
|
|
|
|
def quantize_rnn_cell_modules(module):
|
|
warnings.warn(
|
|
"quantize_rnn_cell_modules function has been deprecated. "
|
|
"Please use torch.ao.quantization.quantize_dynamic API instead."
|
|
)
|
|
reassign = {}
|
|
for name, mod in module.named_modules():
|
|
if mod is module:
|
|
continue
|
|
new_mod = quantize_rnn_cell_modules(mod)
|
|
if new_mod is not mod:
|
|
reassign[name] = new_mod
|
|
for name, mod in reassign.items():
|
|
setattr(module, name, mod)
|
|
if isinstance(module, torch.nn.LSTMCell):
|
|
return QuantizedLSTMCell(module)
|
|
if isinstance(module, torch.nn.GRUCell):
|
|
return QuantizedGRUCell(module)
|
|
if isinstance(module, torch.nn.RNNCell):
|
|
return QuantizedRNNCell(module)
|
|
return module
|
|
|
|
|
|
def quantize_linear_modules(module, dtype=torch.int8):
|
|
warnings.warn(
|
|
"quantize_linear_modules function has been deprecated. "
|
|
"Please use torch.ao.quantization.quantize_dynamic API instead."
|
|
)
|
|
|
|
reassign = {}
|
|
for name, mod in module.named_modules():
|
|
if mod is module:
|
|
continue
|
|
new_mod = quantize_linear_modules(mod, dtype)
|
|
if new_mod is not mod:
|
|
reassign[name] = new_mod
|
|
|
|
for name, mod in reassign.items():
|
|
setattr(module, name, mod)
|
|
if isinstance(module, torch.nn.Linear):
|
|
if dtype == torch.int8:
|
|
return QuantizedLinear(module)
|
|
elif dtype == torch.float16:
|
|
return QuantizedLinearFP16(module)
|
|
else:
|
|
raise RuntimeError(f"Unsupported dtype: {dtype}")
|
|
return module
|
|
|
|
|
|
def quantize_rnn_modules(module, dtype=torch.int8):
|
|
warnings.warn(
|
|
"quantize_rnn_modules function has been deprecated. "
|
|
"Please use torch.ao.quantization.quantize_dynamic API instead."
|
|
)
|
|
reassign = {}
|
|
for name, mod in module.named_modules():
|
|
if mod is module:
|
|
continue
|
|
new_mod = quantize_rnn_modules(mod, dtype)
|
|
if new_mod is not mod:
|
|
reassign[name] = new_mod
|
|
|
|
for name, mod in reassign.items():
|
|
setattr(module, name, mod)
|
|
if isinstance(module, torch.nn.LSTM):
|
|
if dtype != torch.int8 and dtype != torch.float16:
|
|
raise RuntimeError(f"Unsupported dtype: {dtype}")
|
|
return QuantizedLSTM(module, dtype)
|
|
if isinstance(module, torch.nn.GRU):
|
|
return QuantizedGRU(module)
|
|
return module
|