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Fix quantized linear -> quantized fully connected replacement pass + add quantized fully connected per_tensor #6976

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Dec 5, 2024
Merged

Fix quantized linear -> quantized fully connected replacement pass + add quantized fully connected per_tensor #6976

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33 changes: 31 additions & 2 deletions backends/cadence/aot/ops_registrations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -146,7 +146,10 @@
"quantized_fully_connected(Tensor src, Tensor weight, Tensor bias, int src_zero_point, "
"Tensor weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, Tensor? offset) -> (Tensor Z)"
)

lib.define(
"quantized_fully_connected.per_tensor(Tensor src, Tensor weight, Tensor bias, int src_zero_point, "
"int weight_zero_point, int out_multiplier, int out_shift, int out_zero_point, Tensor? offset) -> (Tensor Z)"
)

# ------------------------------------ #
# Migrated from custom_ops.ymal #
Expand Down Expand Up @@ -192,6 +195,10 @@
"quantized_fully_connected.out(Tensor src, Tensor weight, Tensor bias, int src_zero_point, "
"Tensor weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)"
)
lib.define(
"quantized_fully_connected.per_tensor_out(Tensor src, Tensor weight, Tensor bias, int src_zero_point, "
"int weight_zero_point, int out_multiplier, int out_shift, int out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)"
)
lib.define(
"quantized_embedding_byte.out(Tensor weight, Tensor weight_scales, Tensor weight_zero_points, "
"Tensor indices, bool pruned_weights=False, *, Tensor(a!) out) -> Tensor(a!)"
Expand Down Expand Up @@ -595,6 +602,28 @@ def quantized_fully_connected_meta(
bias: torch.Tensor,
in_zero_point: int,
weight_zero_point: torch.Tensor,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_zero_point: int,
offset: Optional[torch.Tensor],
) -> torch.Tensor:
# src comes in shape [leading_dims, in_dim]
# weight comes in shape [out_dim, in_dim]
# output comes in empty with shape [leading_dims, out_dim]
out_size = list(src.size())
weight_size = list(weight.size())
assert len(weight_size) == 2
out_size[-1] = weight_size[0]
return src.new_empty(out_size, dtype=src.dtype)


@register_fake("cadence::quantized_fully_connected.per_tensor")
def quantized_fully_connected_per_tensor_meta(
src: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
in_zero_point: int,
weight_zero_point: int,
out_multiplier: int,
out_shift: int,
out_zero_point: int,
Expand All @@ -607,7 +636,7 @@ def quantized_fully_connected_meta(
weight_size = list(weight.size())
assert len(weight_size) == 2
out_size[-1] = weight_size[0]
return src.new_empty(out_size, dtype=torch.uint8)
return src.new_empty(out_size, dtype=src.dtype)


@register_fake("cadence::convolution")
Expand Down
12 changes: 6 additions & 6 deletions backends/cadence/aot/replace_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,8 @@
# 3. functions that replace an ATen op with another semantically equivalent ATen op.
# 4. functions that concretize optional args.

# pyre-unsafe

import math
from operator import neg
from typing import cast, Dict, Iterable, Sequence, Set, Tuple
Expand Down Expand Up @@ -1698,12 +1700,6 @@ def call_operator(self, op, args, kwargs, meta):
if leading_dims != 1:
return super().call_operator(op, args, kwargs, meta)

# If the op is quantized::linear, but per-channel quantized, bail.
if op == exir_ops.edge.cadence.quantized_linear.default:
weight = args[1].to_tensor() if isinstance(args[1], ProxyValue) else args[1]
if weight.shape != [1]:
return super().call_operator(op, args, kwargs, meta)

# Replace the linear with fully connected op
return super().call_operator(
self.linear_to_fc_op[op],
Expand Down Expand Up @@ -1893,6 +1889,10 @@ class ReplaceSingleElementTensorArgumentsFromFullOpWithScalarPass(ExportPass):
exir_ops.edge.cadence.quantized_conv.per_tensor,
[8, 9, 12, 13],
),
exir_ops.edge.cadence.quantized_fully_connected: (
exir_ops.edge.cadence.quantized_fully_connected.per_tensor,
[4, 5, 6],
),
exir_ops.edge.cadence.quantized_layer_norm: (
exir_ops.edge.cadence.quantized_layer_norm.per_tensor,
[1, 2],
Expand Down