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Define embedding_4bit #3121

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Define embedding_4bit #3121

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134 changes: 131 additions & 3 deletions exir/passes/_quant_patterns_and_replacements.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@
)


def embedding_byte_weight_checks(weight, weight_scales, weight_zero_points):
def embedding_weight_checks(weight, weight_scales, weight_zero_points):
assert weight.dtype in [
torch.int8,
torch.uint8,
Expand Down Expand Up @@ -86,7 +86,7 @@ def embedding_byte(
weight_quant_max: int,
indices: torch.Tensor,
) -> torch.Tensor:
embedding_byte_weight_checks(weight, weight_scales, weight_zero_points)
embedding_weight_checks(weight, weight_scales, weight_zero_points)
group_size = weight.size(1) // (
weight_scales.size(1) if weight_scales.dim() == 2 else 1
)
Expand Down Expand Up @@ -133,7 +133,7 @@ def embedding_byte_dtype(
indices: torch.Tensor,
dtype: Optional[torch.dtype],
) -> torch.Tensor:
embedding_byte_weight_checks(weight, weight_scales, weight_zero_points)
embedding_weight_checks(weight, weight_scales, weight_zero_points)
group_size = weight.size(1) // (
weight_scales.size(1) if weight_scales.dim() == 2 else 1
)
Expand Down Expand Up @@ -172,6 +172,134 @@ def embedding_byte_dtype_out_meta(
)


quantized_decomposed_lib.define(
"embedding_4bit(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
"int weight_quant_min, int weight_quant_max, Tensor indices) -> Tensor",
)

quantized_decomposed_lib.define(
"embedding_4bit.dtype(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
"int weight_quant_min, int weight_quant_max, Tensor indices, ScalarType? dtype=None) -> Tensor",
)

quantized_decomposed_lib.define(
"embedding_4bit.out(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
"int weight_quant_min, int weight_quant_max, Tensor indices, *, Tensor(a!) out) -> Tensor(a!)",
)

quantized_decomposed_lib.define(
"embedding_4bit.dtype_out(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
"int weight_quant_min, int weight_quant_max, Tensor indices, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)",
)


@impl(quantized_decomposed_lib, "embedding_4bit", "CompositeExplicitAutograd")
def embedding_4bit(
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: Optional[torch.Tensor],
weight_quant_min: int,
weight_quant_max: int,
indices: torch.Tensor,
) -> torch.Tensor:
embedding_weight_checks(weight, weight_scales, weight_zero_points)
group_size = (2 * weight.size(1)) // (
weight_scales.size(1) if weight_scales.dim() == 2 else 1
)
weight_even = weight.div(16, rounding_mode="trunc")
weight_odd = weight.remainder(16)
weight_unpacked = torch.stack((weight_even, weight_odd), dim=-1)
weight = weight_unpacked.view(weight.shape[0], -1)
weight = weight.view(torch.int8).add(-8)

weight = torch.ops.quantized_decomposed.dequantize_per_channel_group.default(
weight,
weight_scales,
weight_zero_points,
weight_quant_min,
weight_quant_max,
weight.dtype,
group_size,
weight_scales.dtype,
)
return torch.ops.aten.embedding.default(weight, indices)


@impl_abstract("quantized_decomposed::embedding_4bit.out")
def embedding_4bit_out_meta(
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: Optional[torch.Tensor],
weight_quant_min: int,
weight_quant_max: int,
indices: torch.Tensor,
out: torch.Tensor,
) -> torch.Tensor:
return embedding_4bit(
weight,
weight_scales,
weight_zero_points,
weight_quant_min,
weight_quant_max,
indices,
)


@impl(quantized_decomposed_lib, "embedding_4bit.dtype", "CompositeExplicitAutograd")
def embedding_4bit_dtype(
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: Optional[torch.Tensor],
weight_quant_min: int,
weight_quant_max: int,
indices: torch.Tensor,
dtype: Optional[torch.dtype],
) -> torch.Tensor:
embedding_weight_checks(weight, weight_scales, weight_zero_points)
group_size = (2 * weight.size(1)) // (
weight_scales.size(1) if weight_scales.dim() == 2 else 1
)
weight_even = weight.div(16, rounding_mode="trunc")
weight_odd = weight.remainder(16)
weight_unpacked = torch.stack((weight_even, weight_odd), dim=-1)
weight = weight_unpacked.view(weight.shape[0], -1)
weight = weight.view(torch.int8).add(-8)

weight = torch.ops.quantized_decomposed.dequantize_per_channel_group.default(
weight,
weight_scales,
weight_zero_points,
weight_quant_min,
weight_quant_max,
weight.dtype,
group_size,
dtype,
)
return torch.ops.aten.embedding.default(weight, indices)


@impl_abstract("quantized_decomposed::embedding_4bit.dtype_out")
def embedding_4bit_dtype_out_meta(
weight: torch.Tensor,
weight_scales: torch.Tensor,
weight_zero_points: Optional[torch.Tensor],
weight_quant_min: int,
weight_quant_max: int,
indices: torch.Tensor,
dtype: Optional[torch.dtype],
out: torch.Tensor,
) -> torch.Tensor:
return embedding_4bit_dtype(
weight,
weight_scales,
weight_zero_points,
weight_quant_min,
weight_quant_max,
indices,
dtype,
)


quantized_decomposed_lib.define(
"mixed_mm(Tensor input, Tensor weight, Tensor weight_scales, Tensor? weight_zero_points) -> Tensor",
)
Expand Down