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5c0a380bdf
Summary: This commit adds support for QAT fusion for the [conv-transpose-bn] and [conv-transpose-bn-relu] patterns. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT_ConvBn1d.test_qat_conv_transpose_bn python test/test_quantization.py TestQuantizePT2EQAT_ConvBn1d.test_qat_conv_transpose_bn_relu python test/test_quantization.py TestQuantizePT2EQAT_ConvBn2d.test_qat_conv_transpose_bn python test/test_quantization.py TestQuantizePT2EQAT_ConvBn2d.test_qat_conv_transpose_bn_relu Reviewers: jerryzh168 Subscribers: jerryzh168, supriyar Tasks: https://github.com/pytorch/pytorch/issues/122224 Differential Revision: [D55930704](https://our.internmc.facebook.com/intern/diff/D55930704) Pull Request resolved: https://github.com/pytorch/pytorch/pull/123652 Approved by: https://github.com/jerryzh168
794 lines
32 KiB
Python
794 lines
32 KiB
Python
import dataclasses
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import itertools
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import operator
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from typing import Any, Callable, Dict, List, Tuple, TYPE_CHECKING
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import torch
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from torch.fx import Graph, GraphModule, Node
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from torch.fx.subgraph_rewriter import (
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replace_pattern_with_filters,
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ReplacedPatterns,
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)
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import torch.nn.functional as F
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from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib # noqa: F401
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from torch.ao.quantization.pt2e.export_utils import _WrapperModule
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from torch.ao.quantization.quantizer import (
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DerivedQuantizationSpec,
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EdgeOrNode,
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SharedQuantizationSpec,
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QuantizationSpecBase,
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)
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from .utils import (
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_conv1d_bn_example_inputs,
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_conv2d_bn_example_inputs,
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_is_bn_node,
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_is_conv_or_conv_transpose_node,
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_is_conv_transpose_fn,
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fold_bn_weights_into_conv_node,
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_get_aten_graph_module_for_pattern,
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)
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if TYPE_CHECKING:
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from torch.fx.passes.utils.matcher_with_name_node_map_utils import InternalMatch
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__all__ = [] # type: ignore[var-annotated]
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# Example inputs for quantized and folded conv-bn1d patterns used in convert
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_quantized_conv1d_bn_example_inputs = (
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torch.randn(1, 1, 3), # x
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torch.randn(1, 1, 1), # conv_weight
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torch.randn(1), # bn_weight
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torch.randn(1), # bn_bias
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torch.randn(1), # bn_running_mean
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torch.randn(1), # bn_running_var
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)
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# Example inputs for quantized and folded conv-bn2d patterns used in convert
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_quantized_conv2d_bn_example_inputs = (
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torch.randn(1, 1, 3, 3), # x
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torch.randn(1, 1, 1, 1), # conv_weight
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torch.randn(1), # bn_weight
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torch.randn(1), # bn_bias
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torch.randn(1), # bn_running_mean
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torch.randn(1), # bn_running_var
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)
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def _get_quantized_conv_bn_example_inputs_kwargs(
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is_per_channel: bool,
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has_bias: bool,
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is_cuda: bool,
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) -> Dict[str, Any]:
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"""
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Optional example inputs for quantized and folded conv-bn patterns
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used in convert, expressed as kwargs.
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"""
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kwargs = {}
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# Per tensor quantization uses literals to represent scale and zero
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# point, so there is no need to include them here as kwargs
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if is_per_channel:
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kwargs["scale"] = torch.tensor([1], dtype=torch.float)
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kwargs["zero_point"] = torch.tensor([0], dtype=torch.int)
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if has_bias:
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kwargs["conv_bias"] = torch.randn(1)
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if is_cuda:
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for k, v in kwargs.items():
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if isinstance(v, torch.Tensor):
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kwargs[k] = v.cuda()
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return kwargs
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def _get_conv_bn_pattern(conv_fn: Callable) -> Callable:
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def _conv_bn_pattern(
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x: torch.Tensor,
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conv_weight: torch.Tensor,
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conv_bias: torch.Tensor,
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bn_weight: torch.Tensor,
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bn_bias: torch.Tensor,
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bn_running_mean: torch.Tensor,
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bn_running_var: torch.Tensor,
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) -> torch.Tensor:
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x = conv_fn(x, conv_weight, conv_bias)
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x = F.batch_norm(x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True)
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return x
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return _WrapperModule(_conv_bn_pattern)
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# TODO: merge this with the `no_conv_bias` case
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def _get_qat_conv_bn_pattern(conv_fn: Callable) -> Callable:
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def _qat_conv_bn_pattern(
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x: torch.Tensor,
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conv_weight: torch.Tensor,
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conv_bias: torch.Tensor,
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bn_weight: torch.Tensor,
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bn_bias: torch.Tensor,
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bn_running_mean: torch.Tensor,
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bn_running_var: torch.Tensor,
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) -> torch.Tensor:
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"""
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Approximated method to fuse conv and bn. It requires only one forward pass.
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conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std.
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This is based on `nniqat.ConvBn2d._forward_approximate`.
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"""
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# TODO: allow setting eps
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bn_eps = 1e-5
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running_std = torch.sqrt(bn_running_var + bn_eps)
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scale_factor = bn_weight / running_std
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weight_shape = [1] * len(conv_weight.shape)
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weight_in_channel_axis = 1 if _is_conv_transpose_fn(conv_fn) else 0
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weight_shape[weight_in_channel_axis] = -1
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bias_shape = [1] * len(conv_weight.shape)
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bias_shape[1] = -1
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scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
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zero_bias = torch.zeros_like(conv_bias, dtype=x.dtype)
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x = conv_fn(x, scaled_weight, zero_bias)
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x = x / scale_factor.reshape(bias_shape)
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x = x + conv_bias.reshape(bias_shape)
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x = F.batch_norm(x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True, eps=bn_eps)
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return x
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return _WrapperModule(_qat_conv_bn_pattern)
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def _get_qat_conv_bn_pattern_no_conv_bias(conv_fn: Callable) -> Callable:
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def _qat_conv_bn_pattern_no_conv_bias(
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x: torch.Tensor,
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conv_weight: torch.Tensor,
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# Not used, only for matching convenience
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conv_bias: torch.Tensor,
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bn_weight: torch.Tensor,
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bn_bias: torch.Tensor,
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bn_running_mean: torch.Tensor,
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bn_running_var: torch.Tensor,
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) -> torch.Tensor:
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"""
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Same as `_get_qat_conv_bn_pattern`, but handles the case with no conv bias.
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"""
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# TODO: allow setting eps
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bn_eps = 1e-5
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running_std = torch.sqrt(bn_running_var + bn_eps)
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scale_factor = bn_weight / running_std
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weight_shape = [1] * len(conv_weight.shape)
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weight_in_channel_axis = 1 if _is_conv_transpose_fn(conv_fn) else 0
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weight_shape[weight_in_channel_axis] = -1
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bias_shape = [1] * len(conv_weight.shape)
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bias_shape[1] = -1
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scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
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x = conv_fn(x, scaled_weight, None)
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x = x / scale_factor.reshape(bias_shape)
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x = F.batch_norm(x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True, eps=bn_eps)
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return x
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return _WrapperModule(_qat_conv_bn_pattern_no_conv_bias)
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def _append_qdq(x, is_per_channel, kwargs):
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"""
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Helper function to append q-dq ops after `x`, using dummy values for the qparams
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and qmin/qmax. We use dummy values here because we match with `ignore_literals=True`
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and will manually replace these values after subgraph rewriting.
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Return the dq node.
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"""
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# Dummy args to be passed into q-dq ops
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per_channel_axis = 0
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scale = kwargs["scale"] if is_per_channel else 1.0
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zp = kwargs["zero_point"] if is_per_channel else 0
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qmin = -127
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qmax = 127
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dtype = torch.int8
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qd = torch.ops.quantized_decomposed
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if is_per_channel:
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x = qd.quantize_per_channel(x, scale, zp, per_channel_axis, qmin, qmax, dtype)
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x = qd.dequantize_per_channel(x, scale, zp, per_channel_axis, qmin, qmax, dtype)
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else:
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x = qd.quantize_per_tensor(x, scale, zp, qmin, qmax, dtype)
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x = qd.dequantize_per_tensor(x, scale, zp, qmin, qmax, dtype)
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return x
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def _get_quantized_qat_conv_bn_pattern(
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is_per_channel: bool,
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has_bias: bool,
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bias_is_quantized: bool,
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conv_fn: Callable,
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bn_is_training: bool,
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) -> Callable:
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"""
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Return the quantized version of QAT conv + BN pattern.
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This is based on `nniqat.ConvBn2d._forward_approximate`,
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used in QAT convert. We first match this pattern and replace
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it with the normal [conv - bn] pattern, then fold the BN
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weights into conv.
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"""
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# TODO: allow setting eps
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bn_eps = 1e-5
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def _quantized_qat_conv_bn_pattern(
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x: torch.Tensor,
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conv_weight: torch.Tensor,
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bn_weight: torch.Tensor,
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bn_bias: torch.Tensor,
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bn_running_mean: torch.Tensor,
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bn_running_var: torch.Tensor,
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**kwargs,
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) -> torch.Tensor:
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running_std = torch.sqrt(bn_running_var + bn_eps)
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scale_factor = bn_weight / running_std
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weight_shape = [1] * len(conv_weight.shape)
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weight_shape[0] = -1
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bias_shape = [1] * len(conv_weight.shape)
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bias_shape[1] = -1
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scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
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scaled_weight = _append_qdq(scaled_weight, is_per_channel, kwargs)
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if has_bias:
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zero_bias = torch.zeros_like(kwargs["conv_bias"], dtype=x.dtype)
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if bias_is_quantized:
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zero_bias = _append_qdq(zero_bias, is_per_channel, kwargs)
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x = conv_fn(x, scaled_weight, zero_bias)
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else:
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x = conv_fn(x, scaled_weight, None)
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x = x / scale_factor.reshape(bias_shape)
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if has_bias:
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x = x + kwargs["conv_bias"].reshape(bias_shape)
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x = F.batch_norm(x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=bn_is_training, eps=bn_eps)
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return x
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return _WrapperModule(_quantized_qat_conv_bn_pattern)
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def _get_folded_quantized_qat_conv_bn_pattern(
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is_per_channel: bool,
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has_bias: bool,
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bias_is_quantized: bool,
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conv_fn: Callable,
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bn_is_training: bool,
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) -> Callable:
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"""
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Quantized QAT conv - bn pattern with bn weights being folded into conv.
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"""
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# TODO: allow setting eps
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bn_eps = 1e-5
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def _folded_quantized_qat_conv_bn_pattern(
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x: torch.Tensor,
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conv_weight: torch.Tensor,
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bn_weight: torch.Tensor,
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bn_bias: torch.Tensor,
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bn_running_mean: torch.Tensor,
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bn_running_var: torch.Tensor,
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**kwargs,
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) -> torch.Tensor:
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conv_weight = _append_qdq(conv_weight, is_per_channel, kwargs)
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if has_bias:
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bias = kwargs["conv_bias"]
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if bias_is_quantized:
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bias = _append_qdq(bias, is_per_channel, kwargs)
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else:
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bias = None
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x = conv_fn(x, conv_weight, bias)
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x = F.batch_norm(x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=bn_is_training, eps=bn_eps)
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return x
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return _WrapperModule(_folded_quantized_qat_conv_bn_pattern)
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def _has_conv_bias_filter(
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match: "InternalMatch",
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original_graph: Graph,
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pattern_graph: Graph,
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) -> bool:
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"""
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Match filter for the subgraph rewriter that returns True if the conv node in
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the original graph has bias.
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"""
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for n in match.nodes_map.values():
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if _is_conv_or_conv_transpose_node(n):
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return len(n.args) > 2 and n.args[2] is not None
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raise ValueError("Could not find conv node in matched conv + bn pattern")
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def _no_conv_bias_filter(
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match: "InternalMatch",
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original_graph: Graph,
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pattern_graph: Graph,
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) -> bool:
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"""
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Match filter for the subgraph rewriter that returns True if the conv node in
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the original graph does NOT have bias.
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"""
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return not _has_conv_bias_filter(match, original_graph, pattern_graph)
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def _is_quantize(n: Node) -> bool:
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return n.target in [
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torch.ops.quantized_decomposed.quantize_per_tensor.default,
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torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
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torch.ops.quantized_decomposed.quantize_per_channel.default,
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]
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def _is_dequantize(n: Node) -> bool:
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return n.target in [
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torch.ops.quantized_decomposed.dequantize_per_tensor.default,
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torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
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torch.ops.quantized_decomposed.dequantize_per_channel.default,
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]
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def _get_conv_bn_pattern_nodes(r: ReplacedPatterns) -> Dict[str, Tuple[Node, Node]]:
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"""
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Helper function to extract the nodes in the conv-bn fusion pattern after
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subgraph rewriting, in the form of a map:
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{name: (original_node, replacement_node)}
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The following names must exist in the map:
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"conv", "conv_weight", "conv_input", "bn", "getitem"
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The following names may exist in the map:
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"conv_weight_q", "conv_weight_dq", "conv_bias",
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"conv_bias_q", "conv_bias_dq"
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"""
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def _get_nodes(nodes: List[Node]) -> Tuple[Node, Node, Node]:
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"""
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Return a 3-tuple of (conv_node, bn_node, getitem_node).
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This asserts that the match contains exactly one of each node.
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"""
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conv_node, bn_node, getitem_node = None, None, None
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for n in nodes:
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if n.op != "call_function":
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continue
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if _is_conv_or_conv_transpose_node(n):
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assert conv_node is None
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conv_node = n
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if _is_bn_node(n):
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assert bn_node is None
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bn_node = n
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if n.target == operator.getitem:
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assert getitem_node is None
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getitem_node = n
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assert conv_node is not None
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assert bn_node is not None
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assert getitem_node is not None
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return (conv_node, bn_node, getitem_node)
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def _get_q_dq_nodes(n: Node) -> Tuple[Node, Node, Node]:
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"""
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Return a 3-tuple of (orig_node, q_node, dq_node).
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"""
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assert _is_dequantize(n)
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q_node = n.args[0]
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assert isinstance(q_node, Node)
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assert _is_quantize(q_node)
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orig_node = q_node.args[0]
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assert isinstance(orig_node, Node)
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return (orig_node, q_node, n)
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original_nodes = list(_filter_nodes_map(r.nodes_map).values())
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o_conv, o_bn, o_getitem = _get_nodes(original_nodes)
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r_conv, r_bn, r_getitem = _get_nodes(r.replacements)
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# Create the mapping from original node to replacement node
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mapping = {
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"conv": (o_conv, r_conv),
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"bn": (o_bn, r_bn),
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"getitem": (o_getitem, r_getitem),
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}
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# Extract conv input and weight
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# Note: here we extract the original nodes indirectly through the pattern nodes
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# because the args of the original nodes are no longer available after replacement
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(p_conv, _, _) = _get_nodes(list(r.nodes_map.keys()))
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(p_conv_input, p_conv_weight, *_) = p_conv.args
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(r_conv_input, r_conv_weight, *_) = r_conv.args
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assert isinstance(p_conv_input, Node)
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assert isinstance(p_conv_weight, Node)
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assert isinstance(r_conv_input, Node)
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assert isinstance(r_conv_weight, Node)
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o_conv_input = r.nodes_map[p_conv_input]
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o_conv_weight = r.nodes_map[p_conv_weight]
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# If conv weight is quantized, extract the q - dq nodes
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if _is_dequantize(p_conv_weight):
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p_conv_weight, p_conv_weight_q, p_conv_weight_dq = _get_q_dq_nodes(p_conv_weight)
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r_conv_weight, r_conv_weight_q, r_conv_weight_dq = _get_q_dq_nodes(r_conv_weight)
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o_conv_weight = r.nodes_map[p_conv_weight]
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o_conv_weight_q = r.nodes_map[p_conv_weight_q]
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o_conv_weight_dq = r.nodes_map[p_conv_weight_dq]
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mapping["conv_weight_q"] = (o_conv_weight_q, r_conv_weight_q)
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mapping["conv_weight_dq"] = (o_conv_weight_dq, r_conv_weight_dq)
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mapping["conv_input"] = (o_conv_input, r_conv_input)
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mapping["conv_weight"] = (o_conv_weight, r_conv_weight)
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# Extract conv bias
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if len(p_conv.args) > 2 and len(r_conv.args) > 2:
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p_conv_bias = p_conv.args[2]
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r_conv_bias = r_conv.args[2]
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assert isinstance(p_conv_bias, Node)
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assert isinstance(r_conv_bias, Node)
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o_conv_bias = r.nodes_map[p_conv_bias]
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# If conv bias is quantized, extract the q - dq nodes
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if _is_dequantize(p_conv_bias):
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p_conv_bias, p_conv_bias_q, p_conv_bias_dq = _get_q_dq_nodes(p_conv_bias)
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r_conv_bias, r_conv_bias_q, r_conv_bias_dq = _get_q_dq_nodes(r_conv_bias)
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o_conv_bias = r.nodes_map[p_conv_bias]
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o_conv_bias_q = r.nodes_map[p_conv_bias_q]
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o_conv_bias_dq = r.nodes_map[p_conv_bias_dq]
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mapping["conv_bias_q"] = (o_conv_bias_q, r_conv_bias_q)
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mapping["conv_bias_dq"] = (o_conv_bias_dq, r_conv_bias_dq)
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mapping["conv_bias"] = (o_conv_bias, r_conv_bias)
|
|
return mapping
|
|
|
|
def _filter_nodes_map(nodes_map: Dict[Node, Node]) -> Dict[Node, Node]:
|
|
"""
|
|
Return a filtered `nodes_map` returned from the subgraph rewriter.
|
|
The filtered `nodes_map` will contain only nodes that are actually
|
|
matched in the pattern, excluding None or placeholder nodes.
|
|
"""
|
|
new_nodes_map: Dict[Node, Node] = {}
|
|
for pattern_node, graph_node in nodes_map.items():
|
|
# bias can be None
|
|
if graph_node is None:
|
|
continue
|
|
# skip pattern placeholder nodes
|
|
if pattern_node.op == "placeholder":
|
|
continue
|
|
new_nodes_map[pattern_node] = graph_node
|
|
return new_nodes_map
|
|
|
|
# TODO: this is error prone, use the replace_literals_with_placeholders hack instead
|
|
def _copy_over_literal_conv_args(original_node: Node, new_node: Node):
|
|
"""
|
|
Copy over literal args in conv, such as stride and padding, from the matched node
|
|
in the original graph to its replacement in the new graph.
|
|
|
|
This is needed due to the following limitation in the subgraph rewriter when used
|
|
with dynamo export: literal (non-tensor) args are not supported in the match and
|
|
replacement patterns. This is because dynamo export automatically inlines these
|
|
literal args, making them dead placeholder nodes. In the future, we should check
|
|
if dynamo export can optionally disable this inlining, or if subgraph rewriter
|
|
can do the copying for us. See https://github.com/pytorch/pytorch/issues/100419.
|
|
|
|
Note: Unlike other tensor args like conv weights and biases, literal args are
|
|
preserved in the original nodes after replacement, so we can access them here.
|
|
"""
|
|
assert _is_conv_or_conv_transpose_node(original_node)
|
|
assert _is_conv_or_conv_transpose_node(new_node)
|
|
# x, weight, bias, [stride, padding, dilation, transposed, output_padding, groups]
|
|
new_args = list(new_node.args)
|
|
if len(new_args) < 3:
|
|
# bias is optional, when it is not present, it means it is None
|
|
new_args.append(None)
|
|
new_node.args = tuple(new_args[:3]) + original_node.args[3:]
|
|
|
|
def _update_conv_input_qspec_map_after_replacement(original_node: Node, replacement_node: Node):
|
|
"""
|
|
Update the `input_qspec_map` in the annotation after subgraph rewriting.
|
|
|
|
The original annotation referred to the nodes in the original graph,
|
|
so the keys in the `input_qspec_map` will need to be updated to reflect
|
|
the corresponding nodes in the replacement graph.
|
|
"""
|
|
assert _is_conv_or_conv_transpose_node(original_node)
|
|
assert _is_conv_or_conv_transpose_node(replacement_node)
|
|
if "quantization_annotation" not in original_node.meta:
|
|
return
|
|
original_input_qspec_map = original_node.meta["quantization_annotation"].input_qspec_map
|
|
input_qspec_map = {}
|
|
# get the list of configs, it should be ordered as input, weight, bias
|
|
# note: this is really hacky, we need a better solution, hopefully
|
|
# in subgraph_rewriter, issue tracking the problem: https://github.com/pytorch/pytorch/issues/101820
|
|
all_configs = list(original_input_qspec_map.items())
|
|
# input activation
|
|
input_qspec_map[replacement_node.args[0]] = all_configs[0][1]
|
|
# weight
|
|
input_qspec_map[replacement_node.args[1]] = all_configs[1][1]
|
|
# bias
|
|
if len(replacement_node.args) > 2 and len(all_configs) > 2:
|
|
input_qspec_map[replacement_node.args[2]] = all_configs[2][1]
|
|
replacement_node.meta["quantization_annotation"].input_qspec_map = input_qspec_map
|
|
|
|
def _update_special_qspecs_after_replacement(
|
|
node: Node,
|
|
original_to_replacement_node: Dict[Node, Node],
|
|
):
|
|
"""
|
|
Update the `SharedQuantizationSpec`s and `DerivedQuantizationSpec`s
|
|
used in `node`'s quantization annotation after subgraph rewriting.
|
|
|
|
The original annotation referred to the nodes in the original graph,
|
|
so the nodes used in these special quantization specs will need to
|
|
be updated to the corresponding nodes in the replacement graph.
|
|
"""
|
|
def _get_new_edge_or_node(edge_or_node: EdgeOrNode):
|
|
if isinstance(edge_or_node, Node):
|
|
_node = edge_or_node
|
|
return original_to_replacement_node.get(_node, _node)
|
|
elif isinstance(edge_or_node, tuple) and len(edge_or_node) == 2 and all(isinstance(x, Node) for x in edge_or_node):
|
|
src, dest = edge_or_node
|
|
return (
|
|
original_to_replacement_node.get(src, src),
|
|
original_to_replacement_node.get(dest, dest),
|
|
)
|
|
else:
|
|
raise ValueError("unexpected type for edge_or_node: ", type(edge_or_node))
|
|
|
|
def _get_new_qspec(qspec: QuantizationSpecBase):
|
|
if isinstance(qspec, SharedQuantizationSpec):
|
|
new_edge_or_node = _get_new_edge_or_node(qspec.edge_or_node)
|
|
return SharedQuantizationSpec(new_edge_or_node)
|
|
elif isinstance(qspec, DerivedQuantizationSpec):
|
|
new_derived_from = [_get_new_edge_or_node(x) for x in qspec.derived_from]
|
|
return dataclasses.replace(qspec, derived_from=new_derived_from)
|
|
else:
|
|
return qspec
|
|
|
|
if "quantization_annotation" not in node.meta:
|
|
return
|
|
annotation = node.meta["quantization_annotation"]
|
|
for input_node, qspec in annotation.input_qspec_map.items():
|
|
annotation.input_qspec_map[input_node] = _get_new_qspec(qspec)
|
|
annotation.output_qspec = _get_new_qspec(annotation.output_qspec)
|
|
|
|
def _fuse_conv_bn_qat(m: GraphModule) -> GraphModule:
|
|
has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
|
|
if not has_bn:
|
|
return m
|
|
is_cuda_options = [True, False] if torch.cuda.is_available() else [False]
|
|
for is_cuda in is_cuda_options:
|
|
m = _fuse_conv_bn_qat_helper(m, F.conv1d, _conv1d_bn_example_inputs, is_cuda=is_cuda)
|
|
m = _fuse_conv_bn_qat_helper(m, F.conv2d, _conv2d_bn_example_inputs, is_cuda=is_cuda)
|
|
m = _fuse_conv_bn_qat_helper(m, F.conv_transpose1d, _conv1d_bn_example_inputs, is_cuda=is_cuda)
|
|
m = _fuse_conv_bn_qat_helper(m, F.conv_transpose2d, _conv2d_bn_example_inputs, is_cuda=is_cuda)
|
|
return m
|
|
|
|
def _fuse_conv_bn_qat_helper(
|
|
m: GraphModule,
|
|
conv_fn: Callable,
|
|
example_inputs: Tuple[Any, ...],
|
|
is_cuda: bool,
|
|
) -> GraphModule:
|
|
"""
|
|
Given a graph of decomposed aten ops, replace the (conv + bn) pattern with
|
|
the fused QAT subgraph equivalent. The input graph should already be annotated.
|
|
The annotations in the original nodes will be preserved in the corresponding
|
|
nodes in the new subgraph.
|
|
|
|
Note: This also handles the (conv + bn + relu) pattern.
|
|
"""
|
|
m.graph.eliminate_dead_code()
|
|
m.recompile()
|
|
conv_bn_pattern = _get_conv_bn_pattern(conv_fn)
|
|
match_pattern = _get_aten_graph_module_for_pattern(conv_bn_pattern, example_inputs, is_cuda)
|
|
|
|
# Step (1): Replace patterns with conv bias
|
|
#
|
|
# Here we do replacement separately for cases with and without conv bias, since
|
|
# the replacement patterns for these two cases are substantially different.
|
|
# TODO: use the public replace_pattern API once it also returns replacement nodes
|
|
|
|
qat_conv_bn_pattern = _get_qat_conv_bn_pattern(conv_fn)
|
|
replacement_pattern_with_conv_bias = _get_aten_graph_module_for_pattern(
|
|
qat_conv_bn_pattern,
|
|
example_inputs,
|
|
is_cuda,
|
|
)
|
|
replacements_with_conv_bias = replace_pattern_with_filters(
|
|
m,
|
|
match_pattern,
|
|
replacement_pattern_with_conv_bias,
|
|
match_filters=[_has_conv_bias_filter],
|
|
ignore_literals=True,
|
|
)
|
|
m.recompile()
|
|
|
|
# Step (2): Replace patterns without conv bias
|
|
|
|
qat_conv_bn_pattern_no_conv_bias = _get_qat_conv_bn_pattern_no_conv_bias(conv_fn)
|
|
replacement_pattern_no_conv_bias = _get_aten_graph_module_for_pattern(
|
|
qat_conv_bn_pattern_no_conv_bias,
|
|
example_inputs,
|
|
is_cuda,
|
|
)
|
|
replacements_no_conv_bias = replace_pattern_with_filters(
|
|
m,
|
|
match_pattern,
|
|
replacement_pattern_no_conv_bias,
|
|
match_filters=[_no_conv_bias_filter],
|
|
ignore_literals=True,
|
|
)
|
|
m.recompile()
|
|
|
|
# Step (3): Post processing
|
|
#
|
|
# Due to limited functionality in the subgraph rewriter, here we manually
|
|
# update the replacement graph as follows:
|
|
#
|
|
# (a) Copy over metadata from original subgraph. This ensures the stack traces
|
|
# and annotations are preserved in the new subgraph
|
|
#
|
|
# (b) Copy over literal args for conv from the original subgraph
|
|
# TODO: do this for literal args for batchnorm as well
|
|
#
|
|
# (c) Update all references of the old nodes in the original subgraph to refer
|
|
# to the corresponding nodes in the new subgraph in the annotations
|
|
#
|
|
# In the future, we should try to push as much of this functionality into the
|
|
# subgraph rewriter as possible, so we don't have to manually copy anything over.
|
|
# For more detail, see https://github.com/pytorch/pytorch/issues/100419.
|
|
|
|
all_original_to_replacement_nodes = {}
|
|
for r in replacements_with_conv_bias + replacements_no_conv_bias:
|
|
for original_node, replacement_node in _get_conv_bn_pattern_nodes(r).values():
|
|
# Step (3a): Copy over metadata for all nodes in [conv - bn - getitem]
|
|
replacement_node.meta = original_node.meta
|
|
if _is_conv_or_conv_transpose_node(original_node):
|
|
# Step (3b): Copy over conv literal args
|
|
_copy_over_literal_conv_args(original_node, replacement_node)
|
|
# Step (3c): Update old references in the conv node's input_qspec_map
|
|
_update_conv_input_qspec_map_after_replacement(original_node, replacement_node)
|
|
all_original_to_replacement_nodes[original_node] = replacement_node
|
|
|
|
# Step (3c): Update old references in the special qspecs for all nodes in the graph
|
|
for n in m.graph.nodes:
|
|
_update_special_qspecs_after_replacement(n, all_original_to_replacement_nodes)
|
|
|
|
return m
|
|
|
|
def _duplicate_dequantize_node(m: GraphModule):
|
|
"""
|
|
Helper function to duplicate all dequantize nodes in the graph if the
|
|
node has more than one user. For example:
|
|
|
|
Before:
|
|
quantize -> dequantize -> a
|
|
\\--> b
|
|
\\--> c
|
|
|
|
After:
|
|
quantize -> dequantize_1 -> a
|
|
\\--> dequantize_2 -> b
|
|
\\--> dequantize_3 -> c
|
|
|
|
This is useful for subgraph rewriting. E.g. if we wish to match the
|
|
pattern [dequantize - a] above, subgraph matching would fail because
|
|
the dequantize node has users outside the matched portion of the graph.
|
|
Instead, we match [dequantize_1 - a], which is safe.
|
|
"""
|
|
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor
|
|
for n in m.graph.nodes:
|
|
if n.op != "call_function" or n.target != dq_op or len(n.users) == 1:
|
|
continue
|
|
for user in list(n.users):
|
|
with m.graph.inserting_before(n):
|
|
new_node = m.graph.create_node("call_function", dq_op, n.args, n.kwargs)
|
|
user.replace_input_with(n, new_node)
|
|
m.graph.erase_node(n)
|
|
m.recompile()
|
|
|
|
def _remove_extra_dequantize(m: GraphModule):
|
|
"""
|
|
Removes duplicate dequant nodes in the graph, for an operator that has
|
|
multiple dequant nodes as a user, replace them with a single dequant node
|
|
that can be shared across all the uses. This should be seen as the "reverse"
|
|
of `_duplicate_dequantize_node`.
|
|
"""
|
|
dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor
|
|
for n in m.graph.nodes:
|
|
dq_users = [user for user in n.users if user.op == "call_function" and user.target == dq_op]
|
|
if len(dq_users) > 1:
|
|
with m.graph.inserting_after(dq_users[0]):
|
|
new_node = m.graph.create_node("call_function", dq_op, dq_users[0].args, {})
|
|
for dq_user in dq_users:
|
|
dq_user.replace_all_uses_with(new_node)
|
|
m.graph.erase_node(dq_user)
|
|
m.recompile()
|
|
|
|
def _copy_over_q_dq_args(original_node: Node, replacement_node: Node):
|
|
"""
|
|
Given a pair of quantize or dequantize nodes, copy over all literal args
|
|
from the original node to the replacement node.
|
|
"""
|
|
# For quantize_per_tensor, scale and zp are literals and need to be copied
|
|
# For quantize_per_channel, scale and zp are get_attr nodes and should be skipped
|
|
assert original_node.target == replacement_node.target
|
|
if original_node.target in (
|
|
torch.ops.quantized_decomposed.quantize_per_tensor.default,
|
|
torch.ops.quantized_decomposed.dequantize_per_tensor.default,
|
|
):
|
|
# Args: input, [scale, zp, qmin, qmax, dtype]
|
|
start_copy_arg_index = 1
|
|
elif original_node.target in (
|
|
torch.ops.quantized_decomposed.quantize_per_channel.default,
|
|
torch.ops.quantized_decomposed.dequantize_per_channel.default,
|
|
):
|
|
# Args: input, scale, zp, [axis, qmin, qmax, dtype]
|
|
start_copy_arg_index = 3
|
|
else:
|
|
raise ValueError("Expected quantize/dequantize nodes, got '%s'" % original_node.target)
|
|
replacement_node.args = (
|
|
replacement_node.args[:start_copy_arg_index] + original_node.args[start_copy_arg_index:]
|
|
)
|
|
|
|
def _fold_conv_bn_qat(m: GraphModule) -> GraphModule:
|
|
has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
|
|
if not has_bn:
|
|
return m
|
|
is_cuda_options = [True, False] if torch.cuda.is_available() else [False]
|
|
for is_cuda in is_cuda_options:
|
|
m = _fold_conv_bn_qat_helper(m, F.conv1d, _quantized_conv1d_bn_example_inputs, is_cuda=is_cuda)
|
|
m = _fold_conv_bn_qat_helper(m, F.conv2d, _quantized_conv2d_bn_example_inputs, is_cuda=is_cuda)
|
|
m = _fold_conv_bn_qat_helper(m, F.conv_transpose1d, _quantized_conv1d_bn_example_inputs, is_cuda=is_cuda)
|
|
m = _fold_conv_bn_qat_helper(m, F.conv_transpose2d, _quantized_conv2d_bn_example_inputs, is_cuda=is_cuda)
|
|
return m
|
|
|
|
def _fold_conv_bn_qat_helper(
|
|
m: GraphModule,
|
|
conv_fn: Callable,
|
|
example_inputs: Tuple[Any, ...],
|
|
is_cuda: bool,
|
|
) -> GraphModule:
|
|
"""
|
|
Replace the quantized (conv + bn) pattern with conv with bn weights folded into the weights of conv.
|
|
"""
|
|
m.graph.eliminate_dead_code()
|
|
m.recompile()
|
|
_duplicate_dequantize_node(m)
|
|
|
|
# Step (1): Replace QAT pattern with simple [conv - bn] pattern
|
|
replacements = []
|
|
replacement_options = itertools.product(
|
|
[True, False], # is_per_channel
|
|
[True, False], # has_bias
|
|
[True, False], # bias_is_quantized
|
|
[True, False], # bn_is_training
|
|
)
|
|
for is_per_channel, has_bias, bias_is_quantized, bn_is_training in replacement_options:
|
|
# For the cases without bias, `bias_is_quantized` is irrelevant, so here we arbitrarily
|
|
# filter out one of the values for this flag to avoid having duplicate patterns
|
|
if not has_bias and bias_is_quantized:
|
|
continue
|
|
kwargs = _get_quantized_conv_bn_example_inputs_kwargs(is_per_channel, has_bias, is_cuda)
|
|
match_pattern = _get_quantized_qat_conv_bn_pattern(
|
|
is_per_channel, has_bias, bias_is_quantized, conv_fn, bn_is_training
|
|
)
|
|
match_pattern = _get_aten_graph_module_for_pattern(match_pattern, example_inputs, is_cuda, **kwargs)
|
|
replacement_pattern = _get_folded_quantized_qat_conv_bn_pattern(
|
|
is_per_channel, has_bias, bias_is_quantized, conv_fn, bn_is_training
|
|
)
|
|
replacement_pattern = _get_aten_graph_module_for_pattern(replacement_pattern, example_inputs, is_cuda, **kwargs)
|
|
replacements.extend(
|
|
replace_pattern_with_filters(
|
|
m,
|
|
match_pattern,
|
|
replacement_pattern,
|
|
ignore_literals=True,
|
|
)
|
|
)
|
|
m.recompile()
|
|
_remove_extra_dequantize(m)
|
|
|
|
for r in replacements:
|
|
node_map = _get_conv_bn_pattern_nodes(r)
|
|
|
|
# Step (2): Copy over metadata from original subgraph
|
|
for original_node, replacement_node in node_map.values():
|
|
replacement_node.meta = original_node.meta
|
|
|
|
# Step (3): Copy over args for weight (and optionally bias) q - dq nodes
|
|
_copy_over_q_dq_args(*node_map["conv_weight_q"])
|
|
_copy_over_q_dq_args(*node_map["conv_weight_dq"])
|
|
if "conv_bias_q" in node_map:
|
|
assert "conv_bias_dq" in node_map
|
|
_copy_over_q_dq_args(*node_map["conv_bias_q"])
|
|
_copy_over_q_dq_args(*node_map["conv_bias_dq"])
|
|
|
|
# Step (4): Fold BN weights into conv
|
|
conv_bias = None
|
|
(_, conv_node) = node_map["conv"]
|
|
(_, bn_node) = node_map["bn"]
|
|
(_, conv_weight) = node_map["conv_weight"]
|
|
if "conv_bias" in node_map:
|
|
(_, conv_bias) = node_map["conv_bias"]
|
|
fold_bn_weights_into_conv_node(conv_node, conv_weight, conv_bias, bn_node, m)
|
|
|
|
# Copy over literal args for conv
|
|
for original_node in _filter_nodes_map(r.nodes_map).values():
|
|
if _is_conv_or_conv_transpose_node(original_node):
|
|
_copy_over_literal_conv_args(original_node, conv_node)
|
|
|
|
m.graph.eliminate_dead_code()
|
|
m.recompile()
|
|
return m
|