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[Executorch][sdpa] Setup the structure to enable quantized gemms for sdpa #9912

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Merged
merged 1 commit into from
Apr 4, 2025
Merged

[Executorch][sdpa] Setup the structure to enable quantized gemms for sdpa #9912

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268 changes: 203 additions & 65 deletions extension/llm/custom_ops/op_sdpa.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,51 @@ bool validate_flash_attention_args(
return true;
}

bool validate_cache_quant_params_args(
const Tensor& t,
const Tensor& t_zero_points,
const Tensor& t_scales) {
ET_CHECK_OR_RETURN_FALSE(
t.dim() == t_scales.dim(),
"Quantized tensor and scales must have the same number of dimensions");
ET_CHECK_OR_RETURN_FALSE(
t.dim() == t_zero_points.dim(),
"Quantized tensor and scales must have the same number of dimensions");

ET_CHECK_OR_RETURN_FALSE(
(t.scalar_type() == ScalarType::Char), "Tensor must be of int8_t type");

ET_CHECK_OR_RETURN_FALSE(
(t_scales.scalar_type() == ScalarType::Float),
"Scales tensor must be of float type");

ET_CHECK_OR_RETURN_FALSE(
(t_zero_points.scalar_type() == ScalarType::Char),
"Zero points tensor must be of int8_t type");

// Sizes
for (int64_t i = 0; i < t.dim() - 1; i++) {
ET_CHECK_OR_RETURN_FALSE(
(t.size(i) == t_scales.size(i)),
"Quantized tensor and scales have different shape"
"at dim: %" PRId64 ", t: %zd, t_scales: %zd",
i,
t.size(i),
t_scales.size(i));
;
ET_CHECK_OR_RETURN_FALSE(
(t.size(i) == t_zero_points.size(i)),
"Quantized tensor and zero points have different shape"
"at dim: %" PRId64 ", t: %zd, t_scales: %zd",
i,
t.size(i),
t_zero_points.size(i));
;
}

return true;
}

bool validate_cache_params(
const Tensor& k_cache,
const Tensor& v_cache,
Expand Down Expand Up @@ -233,7 +278,13 @@ Tensor& flash_attention_kernel_out(
dropout_p,
is_causal,
attn_mask,
scale);
scale,
nullopt,
nullopt,
nullopt,
nullopt,
nullopt,
nullopt);
} else if (q_seq_len >= 192) {
sdpa::impl::cpu_flash_attention<CTYPE, 64, 512>(
output,
Expand All @@ -243,7 +294,13 @@ Tensor& flash_attention_kernel_out(
dropout_p,
is_causal,
attn_mask,
scale);
scale,
nullopt,
nullopt,
nullopt,
nullopt,
nullopt,
nullopt);
} else {
sdpa::impl::cpu_flash_attention<CTYPE, 32, 512>(
output,
Expand All @@ -253,28 +310,19 @@ Tensor& flash_attention_kernel_out(
dropout_p,
is_causal,
attn_mask,
scale);
scale,
nullopt,
nullopt,
nullopt,
nullopt,
nullopt,
nullopt);
}
});
return output;
}

/*
Input params
@param[in] q_projected Projected query with query weights.
Format [n_layers, batch size, seq_len, num heads, head dim]
@param[in] k_projected Projected query with key weights.
Format [n_layers, batch size, seq_len, num heads, head dim]
@param[in] v_projected Projected query with value weights.
Format [n_layers, batch size, seq_len, num heads, head dim]
@param[in] key_cache Cache of previous k_projected.
Format [n_layers, batch size, max_seq_len, num heads, head dim]
@param[in] key_cache Cache of previous v_projected.
Format [n_layers, batch size, max_seq_len, num heads, head dim]
....
@param[in] start_pos: sequence position
*/
Tensor& custom_sdpa_out(
Tensor& custom_sdpa_out_impl(
RuntimeContext& ctx,
const Tensor& q,
const Tensor& k,
Expand All @@ -285,7 +333,13 @@ Tensor& custom_sdpa_out(
const bool is_causal,
// @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy
const optional<double> scale,
Tensor& output) {
Tensor& output,
const optional<Tensor>& q_zero_points = nullopt,
const optional<Tensor>& q_scales = nullopt,
const optional<Tensor>& k_zero_points = nullopt,
const optional<Tensor>& k_scales = nullopt,
const optional<Tensor>& v_zero_points = nullopt,
const optional<Tensor>& v_scales = nullopt) {
ET_KERNEL_CHECK_MSG(
ctx,
!attn_mask.has_value() || !is_causal,
Expand All @@ -300,6 +354,40 @@ Tensor& custom_sdpa_out(
output,
"Invalid arguments");

bool is_seq_at_dim_1{true};
if (q.scalar_type() == ScalarType::Char) {
is_seq_at_dim_1 = false;
ET_KERNEL_CHECK_MSG(
ctx,
q_scales.has_value() && q_zero_points.has_value() &&
k_scales.has_value() && k_zero_points.has_value() &&
q_scales.has_value() && q_zero_points.has_value(),
InvalidArgument,
output,
"If q is quantized, k and v must be quantized as well");
ET_KERNEL_CHECK_MSG(
ctx,
validate_cache_quant_params_args(
q, q_zero_points.value(), q_scales.value()),
InvalidArgument,
output,
"Invalid arguments for quantized query");
ET_KERNEL_CHECK_MSG(
ctx,
validate_cache_quant_params_args(
k, k_zero_points.value(), k_scales.value()),
InvalidArgument,
output,
"Invalid arguments for quantized key");
ET_KERNEL_CHECK_MSG(
ctx,
validate_cache_quant_params_args(
v, v_zero_points.value(), v_scales.value()),
InvalidArgument,
output,
"Invalid arguments for quantized value");
}

ET_CHECK_MSG(q.dim() == 4, "query must be a 4D tensor");

const int64_t seq_len = q.size(1);
Expand All @@ -315,53 +403,103 @@ Tensor& custom_sdpa_out(

// TODO(task): replace the template param selection logic
// with whatever apprpriately makes more sense for
ET_SWITCH_FLOAT_TYPES(q.scalar_type(), ctx, "flash_attention", CTYPE, [&] {
// TODO we need to re-evaluate this for ARM CPUs
// And there can be many so instead of templatizing
// we might consider another appraoch
if (q_seq_len >= 768) {
sdpa::impl::cpu_flash_attention<CTYPE, 256, 512>(
output,
q,
k,
v,
dropout_p,
is_causal,
attn_mask,
scale,
true, /* is_seq_at_dim_1 */
start_pos,
num_keys_for_causal_attention);
} else if (q_seq_len >= 192) {
sdpa::impl::cpu_flash_attention<CTYPE, 64, 512>(
output,
q,
k,
v,
dropout_p,
is_causal,
attn_mask,
scale,
true, /* is_seq_at_dim_1 */
start_pos,
num_keys_for_causal_attention);
} else {
sdpa::impl::cpu_flash_attention<CTYPE, 32, 512>(
output,
q,
k,
v,
dropout_p,
is_causal,
attn_mask,
scale,
true, /* is_seq_at_dim_1 */
start_pos,
num_keys_for_causal_attention);
}
});
ET_SWITCH_FLOAT_TYPES(
output.scalar_type(), ctx, "flash_attention", CTYPE, [&] {
// TODO we need to re-evaluate this for ARM CPUs
// And there can be many so instead of templatizing
// we might consider another appraoch
if (q_seq_len >= 768) {
sdpa::impl::cpu_flash_attention<CTYPE, 256, 512>(
output,
q,
k,
v,
dropout_p,
is_causal,
attn_mask,
scale,
nullopt, // q_zero_points
nullopt, // q_scales
nullopt, // k_zero_points
nullopt, // k_scales
nullopt, // v_zero_points
nullopt, // v_scales
is_seq_at_dim_1, /* is_seq_at_dim_1 */
start_pos,
num_keys_for_causal_attention);
} else if (q_seq_len >= 192) {
sdpa::impl::cpu_flash_attention<CTYPE, 64, 512>(
output,
q,
k,
v,
dropout_p,
is_causal,
attn_mask,
scale,
nullopt, // q_zero_points
nullopt, // q_scales
nullopt, // k_zero_points
nullopt, // k_scales
nullopt, // v_zero_points
nullopt, // v_scales
is_seq_at_dim_1, /* is_seq_at_dim_1 */
start_pos,
num_keys_for_causal_attention);
} else {
sdpa::impl::cpu_flash_attention<CTYPE, 32, 512>(
output,
q,
k,
v,
dropout_p,
is_causal,
attn_mask,
scale,
nullopt, // q_zero_points
nullopt, // q_scales
nullopt, // k_zero_points
nullopt, // k_scales
nullopt, // v_zero_points
nullopt, // v_scales
is_seq_at_dim_1, /* is_seq_at_dim_1 */
start_pos,
num_keys_for_causal_attention);
}
});
return output;
}

/*
Input params
@param[in] q_projected Projected query with query weights.
Format [n_layers, batch size, seq_len, num heads, head dim]
@param[in] k_projected Projected query with key weights.
Format [n_layers, batch size, seq_len, num heads, head dim]
@param[in] v_projected Projected query with value weights.
Format [n_layers, batch size, seq_len, num heads, head dim]
@param[in] key_cache Cache of previous k_projected.
Format [n_layers, batch size, max_seq_len, num heads, head dim]
@param[in] key_cache Cache of previous v_projected.
Format [n_layers, batch size, max_seq_len, num heads, head dim]
....
@param[in] start_pos: sequence position
*/
Tensor& custom_sdpa_out(
RuntimeContext& ctx,
const Tensor& q,
const Tensor& k,
const Tensor& v,
const int64_t start_pos,
const optional<Tensor>& attn_mask,
const double dropout_p,
const bool is_causal,
// @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy
const optional<double> scale,
Tensor& output) {
return custom_sdpa_out_impl(
ctx, q, k, v, start_pos, attn_mask, dropout_p, is_causal, scale, output);
}
/*
Input params
@param[in] q_projected Projected query with query weights.
Expand Down
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