Skip to content

Revert D84020397: Group-quantized embedding op #14915

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Oct 8, 2025
Merged

Revert D84020397: Group-quantized embedding op #14915

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 0 additions & 5 deletions backends/cadence/aot/functions.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -468,8 +468,3 @@
kernels:
- arg_meta: null
kernel_name: impl::generic::requantize_per_tensor_out

- func: cadence::quantized_embedding_byte.out(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, Tensor indices, bool pruned_weights, *, Tensor(a!) out) -> Tensor(a!)
kernels:
- arg_meta: null
kernel_name: impl::generic::quantized_embedding_byte_out
26 changes: 2 additions & 24 deletions backends/cadence/aot/ops_registrations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -320,7 +320,7 @@
"float out_scale, int out_zero_point) -> (Tensor Z)"
)
lib.define(
"quantized_embedding_byte(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
"quantized_embedding_byte(Tensor weight, Tensor weight_scales, Tensor weight_zero_points, "
"Tensor indices, bool pruned_weights=False) -> (Tensor X)"
)
lib.define(
Expand Down Expand Up @@ -514,7 +514,7 @@
"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, "
"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 @@ -2310,28 +2310,6 @@ def transposed_im2row_meta(
return input.new_empty(output_size, dtype=input.dtype)


@register_fake("cadence::quantized_embedding_byte")
def quantized_embedding_byte_meta(
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: torch.Tensor | None,
indices: torch.Tensor,
pruned_weights: bool = False,
) -> torch.Tensor:
assert not pruned_weights
assert len(weight.shape) == 2
assert 1 <= len(weight_scales.shape) <= 2
if len(weight_scales.shape) == 2:
num_groups = weight_scales.shape[-1]
assert weight.shape[1] % num_groups == 0

if weight_zero_points is not None:
assert weight_zero_points.shape == weight_scales.shape

assert 1 <= len(indices.shape) <= 2
return torch.empty(*indices.shape, weight.shape[1], dtype=torch.float32)


@register_fake("cadence::where_Scalar")
def where_Scalar_meta(
condition: torch.Tensor,
Expand Down
31 changes: 0 additions & 31 deletions backends/cadence/aot/ref_implementations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1572,34 +1572,3 @@ def transposed_im2row(
# Optionally, flatten to (N, num_patches, patch_size) if needed
patches = patches.view(N, C * H_in * W_in, -1).transpose(1, 2).contiguous()
return patches


@impl(m, "quantized_embedding_byte")
def quantized_embedding_byte(
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: torch.Tensor | None,
indices: torch.Tensor,
pruned_weights: bool = False,
) -> torch.Tensor:
if pruned_weights:
raise NotImplementedError("Pruned weights not supported")

# Cannot use torch.ops.quantized_decomposed.embedding_byte.dtype because
# it doesn't support num_groups == 1
num_groups = 1
if len(weight_scales.shape) == 2:
num_groups = weight_scales.shape[1]

group_size = weight.shape[1] // num_groups
weight = torch.ops.torchao.dequantize_affine.default(
input=weight,
block_size=(1, group_size),
scale=weight_scales,
zero_point=weight_zero_points,
input_dtype=weight.dtype,
quant_min=torch.iinfo(weight.dtype).min,
quant_max=torch.iinfo(weight.dtype).max,
)

return weight[indices]
113 changes: 0 additions & 113 deletions backends/cadence/aot/tests/test_ref_implementations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2306,116 +2306,3 @@ def test_transposed_im2row(
torch.equal(output, expected_output),
f"transposed_im2row output mismatch in {name}: got {output}, expected {expected_output}",
)

@expand(
[
(
"1_group",
torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=torch.int8),
torch.tensor([1, 1, 1], dtype=torch.float32),
torch.tensor([0, 0, 0], dtype=torch.int8),
torch.tensor([0, 2, 1], dtype=torch.int64),
torch.tensor(
[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]],
dtype=torch.float32,
),
),
(
"2_groups",
torch.tensor(
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=torch.int8
),
torch.tensor([[0.5, 1.0], [1.5, 2.0], [2.5, 3.0]], dtype=torch.float32),
torch.tensor([[0, 1], [2, 3], [4, 5]], dtype=torch.int8),
torch.tensor([0, 2, 1], dtype=torch.int64),
torch.tensor(
[
[0.0, 0.5, 1.0, 2.0],
[10.0, 12.5, 15.0, 18.0],
[3.0, 4.5, 6.0, 8.0],
],
dtype=torch.float32,
),
),
(
"1_group_none_zero_point",
torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=torch.int8),
torch.tensor([1, 1, 1], dtype=torch.float32),
None,
torch.tensor([0, 2, 1], dtype=torch.int64),
torch.tensor(
[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]],
dtype=torch.float32,
),
),
(
"1_group_batch2",
torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=torch.int8),
torch.tensor([1, 1, 1], dtype=torch.float32),
torch.tensor([0, 0, 0], dtype=torch.int8),
torch.tensor([[0, 2, 1], [1, 0, 2]], dtype=torch.int64),
torch.tensor(
[
[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]],
[[3.0, 4.0, 5.0], [0.0, 1.0, 2.0], [6.0, 7.0, 8.0]],
],
dtype=torch.float32,
),
),
(
"2_groups_batch2",
torch.tensor(
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=torch.int8
),
torch.tensor([[0.5, 1.0], [1.5, 2.0], [2.5, 3.0]], dtype=torch.float32),
torch.tensor([[0, 1], [2, 3], [4, 5]], dtype=torch.int8),
torch.tensor([[0, 2, 1], [2, 1, 0]], dtype=torch.int64),
torch.tensor(
[
[
[0.0, 0.5, 1.0, 2.0],
[10.0, 12.5, 15.0, 18.0],
[3.0, 4.5, 6.0, 8.0],
],
[
[10.0, 12.5, 15.0, 18.0],
[3.0, 4.5, 6.0, 8.0],
[0.0, 0.5, 1.0, 2.0],
],
],
dtype=torch.float32,
),
),
(
"1_group_none_zero_point_batch2",
torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=torch.int8),
torch.tensor([1, 1, 1], dtype=torch.float32),
None,
torch.tensor([[0, 2, 1], [1, 0, 2]], dtype=torch.int64),
torch.tensor(
[
[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]],
[[3.0, 4.0, 5.0], [0.0, 1.0, 2.0], [6.0, 7.0, 8.0]],
],
dtype=torch.float32,
),
),
]
)
def test_quantized_embedding_byte(
self,
name: str,
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: torch.Tensor | None,
indices: torch.Tensor,
expected_out: torch.Tensor,
) -> None:
self.assertTrue(
torch.equal(
torch.ops.cadence.quantized_embedding_byte(
weight, weight_scales, weight_zero_points, indices
),
expected_out,
)
)
Loading