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Support bf16-out quantized embeddings on CPU #20707

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3 changes: 2 additions & 1 deletion exir/passes/_quant_patterns_and_replacements.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,8 @@ def embedding_weight_checks(weight, weight_scales, weight_zero_points):
assert weight_scales.dtype in [
torch.float16,
torch.float32,
], f"Expecting weight_scales to be of dtype in [torch.float16, torch.float32], but got {weight_scales.dtype}"
torch.bfloat16,
], f"Expecting weight_scales to be of dtype in [torch.float16, torch.float32, torch.bfloat16], but got {weight_scales.dtype}"
assert (
weight_scales.dim() == 1 or weight_scales.dim() == 2
), f"Expecting weight_scales tensor to have rank 1 or 2, but found {weight_scales.dim()}"
Expand Down
27 changes: 25 additions & 2 deletions exir/tests/test_quant_fusion_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -391,7 +391,29 @@ def test_embedding_torchao(self) -> None:
[MappingType.SYMMETRIC, MappingType.ASYMMETRIC],
):
self._test_embedding_torchao(
bit_width, use_dtype_variant, test_per_group, mapping_type
bit_width,
use_dtype_variant,
test_per_group,
mapping_type,
dtype=torch.float16,
)

# bfloat16 mirrors the float16 (dtype-variant) path across bit widths.
for bit_width, test_per_group, mapping_type in zip(
[2, 4, 8],
[True, False, True],
[
MappingType.SYMMETRIC,
MappingType.ASYMMETRIC,
MappingType.SYMMETRIC,
],
):
self._test_embedding_torchao(
bit_width,
use_dtype_variant=True,
test_per_group=test_per_group,
mapping_type=mapping_type,
dtype=torch.bfloat16,
)

def _test_embedding_torchao(
Expand All @@ -400,6 +422,7 @@ def _test_embedding_torchao(
use_dtype_variant: bool,
test_per_group: bool,
mapping_type: MappingType,
dtype: torch.dtype,
) -> None:
assert bit_width in [2, 4, 8]
embedding_suffix = f"{bit_width}bit" if bit_width < 8 else "byte"
Expand All @@ -414,7 +437,7 @@ def _test_embedding_torchao(

# torchao adds a dtype cast to match embeddings original weight type
# this does not happen for float32 because it is the default dtype
model = model.to(torch.float16) if use_dtype_variant else model
model = model.to(dtype) if use_dtype_variant else model

# quantize the model
granularity = PerGroup(32) if test_per_group else PerAxis(0)
Expand Down
57 changes: 32 additions & 25 deletions kernels/quantized/cpu/embeddingxb.cpp
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Expand Up @@ -104,13 +104,15 @@ void check_embedding_xbit_args(

ET_CHECK_MSG(
out.scalar_type() == ScalarType::Float ||
out.scalar_type() == ScalarType::Half,
out.scalar_type() == ScalarType::Half ||
out.scalar_type() == ScalarType::BFloat16,
"out.scalar_type() %" PRId8 " is not supported:",
static_cast<int8_t>(out.scalar_type()));

ET_CHECK_MSG(
weight_scales.scalar_type() == ScalarType::Float ||
weight_scales.scalar_type() == ScalarType::Half,
weight_scales.scalar_type() == ScalarType::Half ||
weight_scales.scalar_type() == ScalarType::BFloat16,
"weight_scales.scalar_type() %" PRId8 " is not supported:",
static_cast<int8_t>(weight_scales.scalar_type()));

Expand Down Expand Up @@ -284,17 +286,19 @@ Tensor& quantized_embedding_xbit_out(

constexpr auto name = "quantized_decomposed::embedding_xbit.out";
ScalarType indices_type = indices.scalar_type();
ET_SWITCH_TWO_TYPES(Float, Half, out_type, ctx, name, CTYPE_OUT, [&]() {
ET_SWITCH_TWO_TYPES(Int, Long, indices_type, ctx, name, CTYPE_IDX, [&]() {
embedding_xbit_per_channel<CTYPE_OUT, CTYPE_OUT, CTYPE_IDX>(
weight,
weight_scales,
opt_weight_zero_points,
indices,
out,
weight_nbit);
});
});
ET_SWITCH_THREE_TYPES(
Float, Half, BFloat16, out_type, ctx, name, CTYPE_OUT, [&]() {
ET_SWITCH_TWO_TYPES(
Int, Long, indices_type, ctx, name, CTYPE_IDX, [&]() {
embedding_xbit_per_channel<CTYPE_OUT, CTYPE_OUT, CTYPE_IDX>(
weight,
weight_scales,
opt_weight_zero_points,
indices,
out,
weight_nbit);
});
});

return out;
}
Expand Down Expand Up @@ -358,19 +362,22 @@ Tensor& quantized_embedding_xbit_dtype_out(

constexpr auto name = "quantized_decomposed::embedding_xbit.dtype_out";
ScalarType indices_type = indices.scalar_type();
ET_SWITCH_TWO_TYPES(Float, Half, params_type, ctx, name, CTYPE_P, [&]() {
ET_SWITCH_TWO_TYPES(Float, Half, out_type, ctx, name, CTYPE_OUT, [&]() {
ET_SWITCH_TWO_TYPES(Int, Long, indices_type, ctx, name, CTYPE_IDX, [&]() {
embedding_xbit_per_channel<CTYPE_P, CTYPE_OUT, CTYPE_IDX>(
weight,
weight_scales,
opt_weight_zero_points,
indices,
out,
weight_nbit);
ET_SWITCH_THREE_TYPES(
Float, Half, BFloat16, params_type, ctx, name, CTYPE_P, [&]() {
ET_SWITCH_THREE_TYPES(
Float, Half, BFloat16, out_type, ctx, name, CTYPE_OUT, [&]() {
ET_SWITCH_TWO_TYPES(
Int, Long, indices_type, ctx, name, CTYPE_IDX, [&]() {
embedding_xbit_per_channel<CTYPE_P, CTYPE_OUT, CTYPE_IDX>(
weight,
weight_scales,
opt_weight_zero_points,
indices,
out,
weight_nbit);
});
});
});
});
});

return out;
}
Expand Down
33 changes: 21 additions & 12 deletions kernels/quantized/cpu/op_embedding.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -66,13 +66,15 @@ void check_embedding_byte_args(

ET_CHECK_MSG(
out.scalar_type() == ScalarType::Float ||
out.scalar_type() == ScalarType::Half,
out.scalar_type() == ScalarType::Half ||
out.scalar_type() == ScalarType::BFloat16,
"out.scalar_type() %" PRId8 " is not supported:",
static_cast<int8_t>(out.scalar_type()));

ET_CHECK_MSG(
weight_scales.scalar_type() == ScalarType::Float ||
weight_scales.scalar_type() == ScalarType::Half,
weight_scales.scalar_type() == ScalarType::Half ||
weight_scales.scalar_type() == ScalarType::BFloat16,
"weight_scales.scalar_type() %" PRId8 " is not supported:",
static_cast<int8_t>(weight_scales.scalar_type()));

Expand Down Expand Up @@ -259,10 +261,11 @@ Tensor& quantized_embedding_byte_out(

constexpr auto name = "quantized_decomposed::embedding_byte.out";
ET_SWITCH_TWO_TYPES(Byte, Char, w_type, ctx, name, CTYPE_W, [&]() {
ET_SWITCH_TWO_TYPES(Float, Half, out_type, ctx, name, CTYPE_OUT, [&]() {
embedding_byte_per_channel<CTYPE_W, CTYPE_OUT, CTYPE_OUT>(
weight, weight_scales, opt_weight_zero_points, indices, out);
});
ET_SWITCH_THREE_TYPES(
Float, Half, BFloat16, out_type, ctx, name, CTYPE_OUT, [&]() {
embedding_byte_per_channel<CTYPE_W, CTYPE_OUT, CTYPE_OUT>(
weight, weight_scales, opt_weight_zero_points, indices, out);
});
});

return out;
Expand Down Expand Up @@ -324,12 +327,18 @@ Tensor& quantized_embedding_byte_dtype_out(

constexpr auto name = "quantized_decomposed::embedding_byte.dtype_out";
ET_SWITCH_TWO_TYPES(Byte, Char, weight_type, ctx, name, CTYPE_W, [&]() {
ET_SWITCH_TWO_TYPES(Float, Half, params_type, ctx, name, CTYPE_P, [&]() {
ET_SWITCH_TWO_TYPES(Float, Half, out_type, ctx, name, CTYPE_OUT, [&]() {
embedding_byte_per_channel<CTYPE_W, CTYPE_P, CTYPE_OUT>(
weight, weight_scales, opt_weight_zero_points, indices, out);
});
});
ET_SWITCH_THREE_TYPES(
Float, Half, BFloat16, params_type, ctx, name, CTYPE_P, [&]() {
ET_SWITCH_THREE_TYPES(
Float, Half, BFloat16, out_type, ctx, name, CTYPE_OUT, [&]() {
embedding_byte_per_channel<CTYPE_W, CTYPE_P, CTYPE_OUT>(
weight,
weight_scales,
opt_weight_zero_points,
indices,
out);
});
});
});

return out;
Expand Down
45 changes: 45 additions & 0 deletions kernels/quantized/test/op_embedding2b_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ using executorch::aten::ScalarType;
using executorch::aten::Tensor;
using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext;
using std::optional;
using torch::executor::native::quantized_embedding_2bit_dtype_out;
using torch::executor::native::quantized_embedding_2bit_out;

using torch::executor::testing::TensorFactory;
Expand Down Expand Up @@ -104,6 +105,50 @@ TEST(OpQuantizedEmbedding2bTest, TestGroupWiseQuantizedEmbedding) {
EXPECT_TENSOR_EQ(out, expected);
}

TEST(OpQuantizedEmbedding2bTest, TestGroupWiseQuantizedEmbeddingBFloat16) {
et_pal_init();
TensorFactory<ScalarType::Byte> tfb;
TensorFactory<ScalarType::BFloat16> tf;
TensorFactory<ScalarType::Long> tfl;

int64_t quant_min = -2;
int64_t quant_max = 1;

Tensor weight_scales = tf.make({3}, {0.5, 1.0, 1.5});
Tensor weight_zero_points = tf.make({3}, {1, -2, 0});
Tensor qweight = tfb.make({3, 1}, {236, 134, 228});
Tensor indices = tfl.make({3}, {0, 2, 1});

Tensor out = tf.zeros({3, 4});
Tensor expected = tf.make(
{3, 4}, {-1.5, 0.0, -0.5, 0.0, -3.0, -1.5, 0.0, 1.5, 2.0, 1.0, 0.0, 2.0});

quantized_embedding_2bit_out(
qweight,
weight_scales,
weight_zero_points,
quant_min,
quant_max,
indices,
out);

EXPECT_TENSOR_CLOSE(out, expected);

// Same values through the dtype_out variant.
out = tf.zeros({3, 4});
quantized_embedding_2bit_dtype_out(
qweight,
weight_scales,
weight_zero_points,
quant_min,
quant_max,
indices,
ScalarType::BFloat16,
out);

EXPECT_TENSOR_CLOSE(out, expected);
}

TEST(OpQuantizedEmbedding2bTest, TestGroupWiseQuantizedEmbeddingInt32Indices) {
et_pal_init();
TensorFactory<ScalarType::Byte> tfb;
Expand Down
45 changes: 45 additions & 0 deletions kernels/quantized/test/op_embedding4b_test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ using executorch::aten::ScalarType;
using executorch::aten::Tensor;
using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext;
using std::optional;
using torch::executor::native::quantized_embedding_4bit_dtype_out;
using torch::executor::native::quantized_embedding_4bit_out;

using torch::executor::testing::TensorFactory;
Expand Down Expand Up @@ -173,6 +174,50 @@ TEST(OpQuantizedEmbedding4bTest, TestGroupWiseQuantizedEmbeddingDeath1) {
"");
}

TEST(OpQuantizedEmbedding4bTest, TestGroupWiseQuantizedEmbeddingBFloat16) {
et_pal_init();
TensorFactory<ScalarType::Byte> tfb;
TensorFactory<ScalarType::BFloat16> tf;
TensorFactory<ScalarType::Long> tfl;

int64_t quant_min = -8;
int64_t quant_max = 7;

Tensor weight_scales = tf.make({3}, {0.5, 1.0, 1.5});
Tensor weight_zero_points = tf.make({3}, {1, -5, 0});
Tensor qweight = tfb.make({3, 2}, {89, 239, 163, 72, 11, 126});
Tensor indices = tfl.make({3}, {0, 2, 1});

Tensor out = tf.zeros({3, 4});
Tensor expected = tf.make(
{3, 4}, {-2.0, 0.0, 2.5, 3.0, -12.0, 4.5, -1.5, 9.0, 7.0, 0.0, 1.0, 5.0});

quantized_embedding_4bit_out(
qweight,
weight_scales,
weight_zero_points,
quant_min,
quant_max,
indices,
out);

EXPECT_TENSOR_CLOSE(out, expected);

// Same values through the dtype_out variant.
out = tf.zeros({3, 4});
quantized_embedding_4bit_dtype_out(
qweight,
weight_scales,
weight_zero_points,
quant_min,
quant_max,
indices,
ScalarType::BFloat16,
out);

EXPECT_TENSOR_CLOSE(out, expected);
}

TEST(OpQuantizedEmbedding4bTest, TestGroupWiseQuantizedEmbeddingDeath2) {
et_pal_init();
TensorFactory<ScalarType::Byte> tfb;
Expand Down
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