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working qsdpa

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Alex Barron
2024-12-06 00:14:24 -08:00
parent e047fd977d
commit 12a4d89a7c
8 changed files with 853 additions and 46 deletions
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96
benchmarks/python/sdpa_vector_bench.py
View File

@@ -1,58 +1,94 @@
import argparse
import math
import mlx.core as mx
import numpy as np
from mlx.utils import tree_map
from time_utils import time_fn
L = 16384
L = 32768
H = 32
H_k = H // 4
D = 128
dtype = mx.float16
loops = 10
bits = 8
loops = 20
def attention(q, k, v):
def _sdpa(q, k, v):
for _ in range(loops):
B, Hq, L, D = q.shape
_, Hk, S, _ = k.shape
q = q.reshape(B, Hk, Hq // Hk, L, D)
k = k[:, :, None, :, :]
v = v[:, :, None, :, :]
s = q @ k.transpose(0, 1, 2, 4, 3)
ke = k[:, :, None, :, :]
ve = v[:, :, None, :, :]
s = q @ ke.transpose(0, 1, 2, 4, 3)
p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
o = p @ v
return o.reshape(B, Hq, L, D)
for i in range(loops):
q = _sdpa(q, k, v)
q = p @ ve
q = q.reshape(B, Hq, L, D)
return q
def sdpa(q, k, v):
for i in range(loops):
q = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0)
for _ in range(loops):
q = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0, mask=None)
return q
def time_self_attention_primitives():
mx.random.seed(3)
q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
mx.eval(q, k, v)
def quant_sdpa(q, k, v, bits=4):
for _ in range(loops):
q = mx.fast.quantized_scaled_dot_product_attention(
q, *k, *v, scale=1.0, mask=None, bits=bits
)
return q
def quant_attention(q, k, v, bits=4):
for _ in range(loops):
B, Hq, L, D = q.shape
Hk = k[0].shape[1]
q = q.reshape((B, Hk, Hq // Hk, L, D))
ke = tree_map(lambda x: mx.expand_dims(x, axis=2), k)
ve = tree_map(lambda x: mx.expand_dims(x, axis=2), v)
scores = mx.quantized_matmul(q, *ke, transpose=True, bits=bits)
scores = mx.softmax(scores, axis=-1)
q = mx.quantized_matmul(scores, *ve, transpose=False, bits=bits)
q = q.reshape((B, Hq, L, D))
return q
def time_self_attention_primitives(q, k, v):
time_fn(attention, q, k, v)
def time_self_attention_sdpa():
mx.random.seed(3)
q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
mx.eval(q, k, v)
def time_self_attention_sdpa(q, k, v):
time_fn(sdpa, q, k, v)
def time_self_attention_quant_sdpa(q, k, v, bits=4):
time_fn(quant_sdpa, q, k, v, bits)
def time_self_attention_quant_primitives(q, k, v, bits=4):
time_fn(quant_attention, q, k, v, bits)
if __name__ == "__main__":
time_self_attention_sdpa()
time_self_attention_primitives()
mx.random.seed(3)
q = mx.random.uniform(shape=(1, H, 1, D), dtype=dtype)
k = mx.random.uniform(shape=(1, H_k, L, D), dtype=dtype)
v = mx.random.uniform(shape=(1, H_k, L, D), dtype=dtype)
mx.eval(q, k, v)
k_quant = mx.quantize(k, bits=bits)
v_quant = mx.quantize(v, bits=bits)
mx.eval(k_quant, v_quant)
k = mx.dequantize(*k_quant, bits=bits)
v = mx.dequantize(*v_quant, bits=bits)
time_self_attention_sdpa(q, k, v)
time_self_attention_quant_sdpa(q, k_quant, v_quant, bits)
time_self_attention_primitives(q, k, v)
time_self_attention_quant_primitives(q, k_quant, v_quant, bits)

29
mlx/backend/metal/kernels/scaled_dot_product_attention.metal
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@@ -20,4 +20,33 @@ using namespace metal;
instantiate_sdpa_vector_heads(float)
instantiate_sdpa_vector_heads(bfloat16_t)
instantiate_sdpa_vector_heads(float16_t)
// Quantized SDPA vector instantiations
#define instantiate_quant_sdpa_vector(name, type, head_dim, group_size, bits) \
instantiate_kernel( \
#name "_" #type "_" #head_dim "_" #group_size "_" #bits, \
name, type, head_dim, group_size, bits)
#define instantiate_quant_sdpa_vector_passes(type, heads, group_size, bits) \
instantiate_quant_sdpa_vector(quant_sdpa_vector, type, heads, group_size, bits) \
instantiate_quant_sdpa_vector(quant_sdpa_vector_2pass_1, type, heads, group_size, bits)
#define instantiate_quant_sdpa_vector_bits(type, heads, group_size) \
instantiate_quant_sdpa_vector_passes(type, heads, group_size, 4) \
instantiate_quant_sdpa_vector_passes(type, heads, group_size, 8)
#define instantiate_quant_sdpa_vector_group_size(type, heads) \
instantiate_quant_sdpa_vector_bits(type, heads, 32) \
instantiate_quant_sdpa_vector_bits(type, heads, 64) \
instantiate_quant_sdpa_vector_bits(type, heads, 128)
#define instantiate_quant_sdpa_vector_heads(type) \
instantiate_quant_sdpa_vector_group_size(type, 64) \
instantiate_quant_sdpa_vector_group_size(type, 96) \
instantiate_quant_sdpa_vector_group_size(type, 128)
instantiate_quant_sdpa_vector_heads(float)
instantiate_quant_sdpa_vector_heads(bfloat16_t)
instantiate_quant_sdpa_vector_heads(float16_t)
// clang-format on

357
mlx/backend/metal/kernels/sdpa_vector.h
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@@ -113,6 +113,208 @@ template <typename T, int D>
}
}
template <typename T, typename U, int elem_per_thread, int bits>
METAL_FUNC U load_queries(const device T* queries, thread U* q, U scale) {
U query_sum = 0;
if (bits == 4) {
for (int i = 0; i < elem_per_thread; i += 4) {
q[i] = scale * queries[i];
q[i + 1] = scale * queries[i + 1];
q[i + 2] = scale * queries[i + 2];
q[i + 3] = scale * queries[i + 3];
query_sum += q[i] + q[i + 1] + q[i + 2] + q[i + 3];
q[i + 1] /= 16.0f;
q[i + 2] /= 256.0f;
q[i + 3] /= 4096.0f;
}
} else if (bits == 8) {
for (int i = 0; i < elem_per_thread; i++) {
q[i] = scale * queries[i];
query_sum += q[i];
}
}
return query_sum;
}
template <typename U, int elem_per_thread, int bits>
METAL_FUNC void load_keys(const device uint32_t* keys, thread U* k) {
if (bits == 4) {
auto ks = (const device uint16_t*)keys;
for (int i = 0; i < elem_per_thread / 4; i++) {
k[4 * i] = ks[i] & 0x000f;
k[4 * i + 1] = ks[i] & 0x00f0;
k[4 * i + 2] = ks[i] & 0x0f00;
k[4 * i + 3] = ks[i] & 0xf000;
}
} else if (bits == 8) {
auto ks = (const device uint8_t*)keys;
for (int i = 0; i < elem_per_thread; i++) {
k[i] = ks[i];
}
}
}
template <typename U, int elem_per_thread, int bits>
METAL_FUNC void load_values(
const device uint32_t* values,
thread U* v,
U value_scale,
U value_bias) {
auto vs = (const device uint8_t*)values;
if (bits == 4) {
U s[2] = {value_scale, value_scale / 16.0f};
for (int i = 0; i < elem_per_thread / 2; i++) {
v[2 * i] = s[0] * (vs[i] & 0x0f) + value_bias;
v[2 * i + 1] = s[1] * (vs[i] & 0xf0) + value_bias;
}
} else if (bits == 8) {
for (int i = 0; i < elem_per_thread; i++) {
v[i] = value_scale * vs[i] + value_bias;
}
}
}
template <typename T, int D, int group_size, int bits>
[[kernel]] void quant_sdpa_vector(
const device T* queries [[buffer(0)]],
const device uint32_t* keys [[buffer(1)]],
const device T* key_scales [[buffer(2)]],
const device T* key_biases [[buffer(3)]],
const device uint32_t* values [[buffer(4)]],
const device T* value_scales [[buffer(5)]],
const device T* value_biases [[buffer(6)]],
device T* out [[buffer(7)]],
const constant int& gqa_factor,
const constant int& N,
const constant size_t& k_stride,
const constant size_t& group_stride,
const constant float& scale,
uint3 tid [[threadgroup_position_in_grid]],
uint simd_gid [[simdgroup_index_in_threadgroup]],
uint simd_lid [[thread_index_in_simdgroup]],
uint quad_gid [[quadgroup_index_in_threadgroup]],
uint quad_lid [[thread_index_in_quadgroup]]) {
constexpr int BN = 32;
constexpr int BD = 4;
constexpr int elem_per_thread = D / BD;
constexpr int pack_factor = 32 / bits;
const int stride = BN * D;
typedef float U;
thread U q[elem_per_thread];
thread U k[elem_per_thread];
thread U v[elem_per_thread];
thread U o[elem_per_thread];
threadgroup U outputs[BN * BD];
threadgroup U max_scores[BN];
threadgroup U sum_exp_scores[BN];
// Adjust positions
const int head_idx = tid.y;
const int kv_head_idx = head_idx / gqa_factor;
queries += head_idx * D + quad_lid * elem_per_thread;
const int kv_idx = quad_gid * D + quad_lid * elem_per_thread;
const int packed_idx = kv_head_idx * k_stride + kv_idx / pack_factor;
const int group_idx = kv_head_idx * group_stride + kv_idx / group_size;
keys += packed_idx;
key_scales += group_idx;
key_biases += group_idx;
values += packed_idx;
value_scales += group_idx;
value_biases += group_idx;
out += head_idx * D + simd_gid * elem_per_thread;
// Read the query and 0 the output accumulator
U query_sum = load_queries<T, U, elem_per_thread, bits>(
queries, q, static_cast<U>(scale));
for (int i = 0; i < elem_per_thread; i++) {
o[i] = 0;
}
U max_score = -INFINITY;
U sum_exp_score = 0;
// For each key
for (int i = quad_gid; i < N; i += BN) {
load_keys<U, elem_per_thread, bits>(keys, k);
// Assume D % group_size == 0 so all the keys are in the same group
U key_scale = key_scales[0];
U key_bias = key_biases[0];
// Compute the i-th score
U score = 0;
for (int i = 0; i < elem_per_thread; i++) {
score += q[i] * k[i];
}
score = score * key_scale + query_sum * key_bias;
score = quad_sum(score);
// Update the accumulators
U new_max = max(max_score, score);
U factor = fast::exp(max_score - new_max);
U exp_score = fast::exp(score - new_max);
max_score = new_max;
sum_exp_score = sum_exp_score * factor + exp_score;
U value_scale = value_scales[0];
U value_bias = value_biases[0];
// Load the values
load_values<U, elem_per_thread, bits>(values, v, value_scale, value_bias);
// Update the output accumulator
for (int i = 0; i < elem_per_thread; i++) {
o[i] = o[i] * factor + exp_score * v[i];
}
// Move the pointers to the next kv
keys += stride / pack_factor;
key_scales += stride / group_size;
key_biases += stride / group_size;
values += stride / pack_factor;
value_scales += stride / group_size;
value_biases += stride / group_size;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// Each thread has a partial part of the output so we need to combine them.
// First let's communicate the max and sum_exp
// Each quadgroup communicates it's max score
if (quad_lid == 0) {
max_scores[quad_gid] = max_score;
sum_exp_scores[quad_gid] = sum_exp_score;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
max_score = max_scores[simd_lid];
U new_max = simd_max(max_score);
U factor = fast::exp(max_score - new_max);
sum_exp_score = simd_sum(sum_exp_scores[simd_lid] * factor);
// Now we need to aggregate all the outputs
for (int i = 0; i < elem_per_thread; i++) {
// 128 threads with 32 values per thread
outputs[simd_gid * BN + simd_lid] = o[i];
threadgroup_barrier(mem_flags::mem_threadgroup);
o[i] = simd_sum(outputs[simd_lid * BD + simd_gid] * factor) / sum_exp_score;
threadgroup_barrier(mem_flags::mem_threadgroup);
}
// And write the output
if (simd_lid == 0) {
for (int i = 0; i < elem_per_thread; i++) {
out[i] = static_cast<T>(o[i]);
}
}
}
template <typename T, int D>
[[kernel]] void sdpa_vector_2pass_1(
const device T* queries [[buffer(0)]],
@@ -290,3 +492,158 @@ template <typename T, int D>
}
}
}
template <typename T, int D, int group_size, int bits>
[[kernel]] void quant_sdpa_vector_2pass_1(
const device T* queries [[buffer(0)]],
const device uint32_t* keys [[buffer(1)]],
const device T* key_scales [[buffer(2)]],
const device T* key_biases [[buffer(3)]],
const device uint32_t* values [[buffer(4)]],
const device T* value_scales [[buffer(5)]],
const device T* value_biases [[buffer(6)]],
device float* out [[buffer(7)]],
device float* sums [[buffer(8)]],
device float* maxs [[buffer(9)]],
const constant int& gqa_factor,
const constant int& N,
const constant size_t& k_stride,
const constant size_t& v_stride,
const constant size_t& k_group_stride,
const constant size_t& v_group_stride,
const constant float& scale,
uint3 tid [[threadgroup_position_in_grid]],
uint simd_gid [[simdgroup_index_in_threadgroup]],
uint simd_lid [[thread_index_in_simdgroup]],
uint quad_gid [[quadgroup_index_in_threadgroup]],
uint quad_lid [[thread_index_in_quadgroup]]) {
constexpr int BN = 8;
constexpr int BD = 4;
constexpr int elem_per_thread = D / BD;
const int stride = BN * D;
constexpr int blocks = 32;
constexpr int pack_factor = 32 / bits;
typedef float U;
thread U q[elem_per_thread];
thread U k[elem_per_thread];
thread U v[elem_per_thread];
thread U o[elem_per_thread];
threadgroup U outputs[BN * BD];
threadgroup U max_scores[BN];
threadgroup U sum_exp_scores[BN];
// Adjust positions
const int block_idx = tid.z;
const int head_idx = tid.y;
const int kv_head_idx = head_idx / gqa_factor;
queries += head_idx * D + quad_lid * elem_per_thread;
const int kv_idx =
(block_idx * BN + quad_gid) * D + quad_lid * elem_per_thread;
const int packed_idx = kv_idx / pack_factor;
const int k_group_idx = kv_head_idx * k_group_stride + kv_idx / group_size;
const int v_group_idx = kv_head_idx * v_group_stride + kv_idx / group_size;
keys += kv_head_idx * k_stride + packed_idx;
key_scales += k_group_idx;
key_biases += k_group_idx;
values += kv_head_idx * v_stride + packed_idx;
value_scales += v_group_idx;
value_biases += v_group_idx;
out += head_idx * blocks * D + block_idx * D + quad_lid * elem_per_thread;
sums += head_idx * blocks + block_idx;
maxs += head_idx * blocks + block_idx;
// Read the query and 0 the output accumulator
U query_sum = load_queries<T, U, elem_per_thread, bits>(
queries, q, static_cast<U>(scale));
for (int i = 0; i < elem_per_thread; i++) {
o[i] = 0;
}
U max_score = -1e9;
U sum_exp_score = 0;
// For each key
for (int i = block_idx * BN + quad_gid; i < N; i += blocks * BN) {
// Read the key
load_keys<U, elem_per_thread, bits>(keys, k);
// Assume D % group_size == 0 so all the keys are in the same group
U key_scale = key_scales[0];
U key_bias = key_biases[0];
// Compute the i-th score
U score = 0;
for (int i = 0; i < elem_per_thread; i++) {
score += q[i] * k[i];
}
score = score * key_scale + query_sum * key_bias;
score = quad_sum(score);
// Update the accumulators
U new_max = max(max_score, score);
U factor = fast::exp(max_score - new_max);
U exp_score = fast::exp(score - new_max);
max_score = new_max;
sum_exp_score = sum_exp_score * factor + exp_score;
U value_scale = value_scales[0];
U value_bias = value_biases[0];
load_values<U, elem_per_thread, bits>(values, v, value_scale, value_bias);
// Update the output accumulator
for (int i = 0; i < elem_per_thread; i++) {
o[i] = o[i] * factor + exp_score * v[i];
}
// Move the pointers to the next kv
keys += blocks * stride / pack_factor;
key_scales += blocks * stride / group_size;
key_biases += blocks * stride / group_size;
values += blocks * stride / pack_factor;
value_scales += blocks * stride / group_size;
value_biases += blocks * stride / group_size;
}
// Each thread has a partial part of the output so we need to combine them.
// First let's communicate the max and sum_exp
if (quad_lid == 0) {
max_scores[quad_gid] = max_score;
sum_exp_scores[quad_gid] = sum_exp_score;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
max_score = (simd_lid < BN) ? max_scores[simd_lid] : -1e9;
U new_max = simd_max(max_score);
U factor = fast::exp(max_score - new_max);
sum_exp_score = (simd_lid < BN) ? sum_exp_scores[simd_lid] : 0;
sum_exp_score = simd_sum(sum_exp_score * factor);
// Write the sum and new max
if (simd_gid == 0) {
sums[0] = sum_exp_score;
maxs[0] = new_max;
}
// Now we need to aggregate all the outputs
for (int i = 0; i < elem_per_thread; i++) {
outputs[quad_lid * BN + quad_gid] =
o[i] * fast::exp(max_scores[quad_gid] - new_max);
threadgroup_barrier(mem_flags::mem_threadgroup);
if (quad_gid == 0) {
U output = outputs[quad_lid * BN];
for (int j = 1; j < BN; j++) {
output += outputs[quad_lid * BN + j];
}
out[i] = static_cast<T>(output);
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
}

211
mlx/backend/metal/scaled_dot_product_attention.cpp
View File

@@ -242,6 +242,171 @@ void sdpa_vector_2pass(
compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
}
void quant_sdpa_vector(
const Stream& s,
metal::Device& d,
const array& q,
const array& k,
const array& k_scales,
const array& k_biases,
const array& v,
const array& v_scales,
const array& v_biases,
array& out,
float scale,
int group_size,
int bits) {
// Set the kernel name
std::string kname;
kname.reserve(96);
kname += "quant_sdpa_vector_";
kname += get_type_string(q.dtype());
kname += "_";
kname += std::to_string(q.shape(-1));
kname += "_";
kname += std::to_string(group_size);
kname += "_";
kname += std::to_string(bits);
// Compute the necessary sizes
int gqa_factor = q.shape(1) / k.shape(1);
int N = k.shape(2);
int B = q.shape(0) * q.shape(1);
size_t stride = k.strides()[1];
size_t group_stride = k_scales.strides()[1];
MTL::Size group_dims(128, 1, 1);
MTL::Size grid_dims(1, B, 1);
// Get the kernel
auto& compute_encoder = d.get_command_encoder(s.index);
auto kernel = d.get_kernel(kname);
compute_encoder.set_compute_pipeline_state(kernel);
// Set its arguments
compute_encoder.set_input_array(q.data_shared_ptr() == nullptr ? out : q, 0);
compute_encoder.set_input_array(k, 1);
compute_encoder.set_input_array(k_scales, 2);
compute_encoder.set_input_array(k_biases, 3);
compute_encoder.set_input_array(v, 4);
compute_encoder.set_input_array(v_scales, 5);
compute_encoder.set_input_array(v_biases, 6);
compute_encoder.set_output_array(out, 7);
compute_encoder.set_bytes(&gqa_factor, sizeof(int), 8);
compute_encoder.set_bytes(&N, sizeof(int), 9);
compute_encoder.set_bytes(&stride, sizeof(size_t), 10);
compute_encoder.set_bytes(&group_stride, sizeof(size_t), 11);
compute_encoder.set_bytes(&scale, sizeof(float), 12);
// Launch
compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
}
void quant_sdpa_vector_2pass(
const Stream& s,
metal::Device& d,
const array& q,
const array& k,
const array& k_scales,
const array& k_biases,
const array& v,
const array& v_scales,
const array& v_biases,
array& out,
float scale,
int group_size,
int bits) {
// Set the kernel name
std::string kname;
kname.reserve(96);
kname += "quant_sdpa_vector_2pass_1_";
kname += get_type_string(q.dtype());
kname += "_";
kname += std::to_string(q.shape(-1));
kname += "_";
kname += std::to_string(group_size);
kname += "_";
kname += std::to_string(bits);
// Compute the necessary sizes
int gqa_factor = q.shape(1) / k.shape(1);
int N = k.shape(2);
int blocks = 32;
int B = q.shape(0) * q.shape(1);
size_t k_stride = k.strides()[1];
size_t v_stride = v.strides()[1];
size_t k_group_stride = k_scales.strides()[1];
size_t v_group_stride = v_scales.strides()[1];
MTL::Size group_dims(8 * 4, 1, 1);
MTL::Size grid_dims(1, B, blocks);
// Allocate the intermediates
std::vector<int> intermediate_shape;
intermediate_shape.reserve(out.ndim() + 1);
intermediate_shape.insert(
intermediate_shape.end(), out.shape().begin(), out.shape().end() - 1);
intermediate_shape.push_back(blocks);
intermediate_shape.push_back(out.shape().back());
array intermediate(intermediate_shape, float32, nullptr, {});
intermediate_shape.pop_back();
array sums(intermediate_shape, float32, nullptr, {});
array maxs(std::move(intermediate_shape), float32, nullptr, {});
intermediate.set_data(allocator::malloc_or_wait(intermediate.nbytes()));
sums.set_data(allocator::malloc_or_wait(sums.nbytes()));
maxs.set_data(allocator::malloc_or_wait(maxs.nbytes()));
d.add_temporary(intermediate, s.index);
d.add_temporary(sums, s.index);
d.add_temporary(maxs, s.index);
// Get the kernel
auto& compute_encoder = d.get_command_encoder(s.index);
auto kernel = d.get_kernel(kname);
compute_encoder.set_compute_pipeline_state(kernel);
// Set its arguments
compute_encoder.set_input_array(q.data_shared_ptr() == nullptr ? out : q, 0);
compute_encoder.set_input_array(k, 1);
compute_encoder.set_input_array(k_scales, 2);
compute_encoder.set_input_array(k_biases, 3);
compute_encoder.set_input_array(v, 4);
compute_encoder.set_input_array(v_scales, 5);
compute_encoder.set_input_array(v_biases, 6);
compute_encoder.set_output_array(intermediate, 7);
compute_encoder.set_output_array(sums, 8);
compute_encoder.set_output_array(maxs, 9);
compute_encoder.set_bytes(gqa_factor, 10);
compute_encoder.set_bytes(N, 11);
compute_encoder.set_bytes(k_stride, 12);
compute_encoder.set_bytes(v_stride, 13);
compute_encoder.set_bytes(k_group_stride, 14);
compute_encoder.set_bytes(v_group_stride, 15);
compute_encoder.set_bytes(scale, 16);
// Launch
compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
// Final pass
kname.clear();
kname += "sdpa_vector_2pass_2_";
kname += get_type_string(q.dtype());
kname += "_";
kname += std::to_string(q.shape(-1));
// Get the kernel
kernel = d.get_kernel(kname);
compute_encoder.set_compute_pipeline_state(kernel);
// Set its arguments
compute_encoder.set_input_array(intermediate, 0);
compute_encoder.set_input_array(sums, 1);
compute_encoder.set_input_array(maxs, 2);
compute_encoder.set_output_array(out, 3);
// Launch
group_dims = MTL::Size(1024, 1, 1);
grid_dims = MTL::Size(1, B, 1);
compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
}
} // namespace
void ScaledDotProductAttention::eval_gpu(
@@ -254,7 +419,6 @@ void ScaledDotProductAttention::eval_gpu(
auto& q_pre = inputs[0];
auto& k_pre = inputs[1];
auto& v_pre = inputs[2];
auto& o = out;
std::vector<array> copies;
@@ -295,9 +459,7 @@ void ScaledDotProductAttention::eval_gpu(
// We are in vector mode ie single query
if (q_pre.shape(2) == 1) {
const auto& q = copy_unless(is_contiguous, q_pre);
const auto& k = copy_unless(is_contiguous_except_seq_len, k_pre);
const auto& v = copy_unless(is_contiguous_except_seq_len, v_pre);
auto q = copy_unless(is_contiguous, q_pre);
// Donate the query if possible
if (q.is_donatable()) {
@@ -306,9 +468,41 @@ void ScaledDotProductAttention::eval_gpu(
o.set_data(allocator::malloc_or_wait(o.nbytes()));
}
// We route to the 2 pass fused attention if
// - The device is large and the sequence length long
// - The sequence length is even longer and we have gqa
if (quantized_) {
auto& k_scales_pre = inputs[2];
auto& k_biases_pre = inputs[3];
auto& v_pre = inputs[4];
auto& v_scales_pre = inputs[5];
auto& v_biases_pre = inputs[6];
auto k = copy_unless(is_contiguous_except_seq_len, k_pre);
auto k_scales = copy_unless(is_contiguous_except_seq_len, k_scales_pre);
auto k_biases = copy_unless(is_contiguous_except_seq_len, k_biases_pre);
auto v = copy_unless(is_contiguous_except_seq_len, v_pre);
auto v_scales = copy_unless(is_contiguous_except_seq_len, v_scales_pre);
auto v_biases = copy_unless(is_contiguous_except_seq_len, v_biases_pre);
quant_sdpa_vector_2pass(
s,
d,
q,
k,
k_scales,
k_biases,
v,
v_scales,
v_biases,
o,
scale_,
group_size_,
bits_);
} else {
auto& k_pre = inputs[1];
auto& v_pre = inputs[2];
const auto& k = copy_unless(is_contiguous_except_seq_len, k_pre);
const auto& v = copy_unless(is_contiguous_except_seq_len, v_pre);
char devc = d.get_architecture().back();
if ((devc == 'd' && k.shape(2) >= 1024) ||
(k.shape(1) < q.shape(1) && k.shape(2) >= 4096)) {
@@ -317,9 +511,12 @@ void ScaledDotProductAttention::eval_gpu(
sdpa_vector(s, d, q, k, v, o, scale_);
}
}
}
// Full attention mode
else {
auto& v_pre = inputs[2];
const auto& q = copy_unless(is_matrix_contiguous, q_pre);
const auto& k = copy_unless(is_matrix_contiguous, k_pre);
const auto& v = copy_unless(is_matrix_contiguous, v_pre);

127
mlx/fast.cpp
View File

@@ -664,7 +664,7 @@ array scaled_dot_product_attention(
std::move(out_shape),
final_type,
std::make_shared<ScaledDotProductAttention>(
stream, fallback, scale, false),
stream, fallback, scale, /*needs_mask=*/false, /*quantized=*/false),
{q, k, v});
}
@@ -678,7 +678,130 @@ array scaled_dot_product_attention(
bool ScaledDotProductAttention::is_equivalent(const Primitive& other) const {
const ScaledDotProductAttention& a_other =
static_cast<const ScaledDotProductAttention&>(other);
return needs_mask_ == a_other.needs_mask_ && scale_ == a_other.scale_;
return needs_mask_ == a_other.needs_mask_ && scale_ == a_other.scale_ &&
quantized_ == a_other.quantized_;
}
array quantized_scaled_dot_product_attention(
const array& queries,
const array& keys,
const array& key_scales,
const array& key_biases,
const array& values,
const array& value_scales,
const array& value_biases,
const float scale,
const std::optional<array>& mask,
const int group_size,
const int bits,
StreamOrDevice s) {
int el_per_int = 32 / bits;
int out_dim = values.shape(-1) * el_per_int;
auto n_q_heads = queries.shape(-3);
auto n_kv_heads = keys.shape(-3);
auto out_shape = std::vector<int>(
{queries.shape(0), queries.shape(1), queries.shape(2), out_dim});
auto stream = to_stream(s);
bool needs_mask = mask.has_value();
auto fallback =
[scale, needs_mask, n_q_heads, n_kv_heads, group_size, bits, &s](
const std::vector<array>& inputs) -> std::vector<array> {
int n_repeats = n_q_heads / n_kv_heads;
auto q = multiply(array(scale, inputs[0].dtype()), inputs[0], s);
auto k = inputs[1];
auto k_scales = inputs[2];
auto k_biases = inputs[3];
auto v = inputs[4];
auto v_scales = inputs[5];
auto v_biases = inputs[6];
int B = q.shape(0);
int L = q.shape(2);
if (n_repeats > 1) {
q = reshape(q, {B, n_kv_heads, n_repeats, L, -1}, s);
k = expand_dims(k, 2, s);
k_scales = expand_dims(k_scales, 2, s);
k_biases = expand_dims(k_biases, 2, s);
v = expand_dims(v, 2, s);
v_scales = expand_dims(v_scales, 2, s);
v_biases = expand_dims(v_biases, 2, s);
}
array scores = quantized_matmul(
q,
k,
k_scales,
k_biases,
/*transpose=*/true,
/*group_size=*/group_size,
/*bits=*/bits,
s);
if (needs_mask) {
scores = add(scores, inputs[7], s);
}
scores = softmax(scores, std::vector<int>{-1}, true, s);
array out = quantized_matmul(
scores,
v,
v_scales,
v_biases,
/*transpose=*/false,
/*group_size=*/group_size,
/*bits=*/bits,
s);
if (n_repeats > 1) {
out = reshape(out, {B, n_q_heads, L, -1}, s);
}
return std::vector<array>{out};
};
int L = queries.shape(2);
if (L > 1) {
if (needs_mask) {
return fallback(
{queries,
keys,
key_scales,
key_biases,
values,
value_scales,
value_biases,
mask.value()})[0];
} else {
return fallback(
{queries,
keys,
key_scales,
key_biases,
values,
value_scales,
value_biases})[0];
}
} else {
return array(
std::move(out_shape),
queries.dtype(),
std::make_shared<ScaledDotProductAttention>(
stream,
fallback,
scale,
/*needs_mask=*/false,
/*quantized=*/true,
group_size,
bits),
{queries,
keys,
key_scales,
key_biases,
values,
value_scales,
value_biases});
}
}
array pack_and_quantize(

15
mlx/fast.h
View File

@@ -41,6 +41,21 @@ array scaled_dot_product_attention(
const std::optional<int> memory_efficient_threshold = std::nullopt,
StreamOrDevice s = {});
/** Computes: `O = softmax(Q @ K.T) @ V` where K and V are quantized. **/
array quantized_scaled_dot_product_attention(
const array& queries,
const array& keys,
const array& key_scales,
const array& key_biases,
const array& values,
const array& value_scales,
const array& value_biases,
const float scale,
const std::optional<array>& mask = std::nullopt,
const int group_size = 64,
const int bits = 4,
StreamOrDevice s = {});
std::tuple<array, array, array> affine_quantize(
const array& w,
int group_size = 64,

15
mlx/fast_primitives.h
View File

@@ -190,8 +190,16 @@ class ScaledDotProductAttention : public Custom {
Stream stream,
std::function<std::vector<array>(std::vector<array>)> fallback,
const float scale,
const bool needs_mask)
: Custom(stream, fallback), scale_(scale), needs_mask_(needs_mask) {}
const bool needs_mask,
const bool quantized,
const int group_size = 64,
const int bits = 4)
: Custom(stream, fallback),
scale_(scale),
needs_mask_(needs_mask),
quantized_(quantized),
group_size_(group_size),
bits_(bits) {}
void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
override {
@@ -212,6 +220,9 @@ class ScaledDotProductAttention : public Custom {
std::function<std::vector<array>(std::vector<array>)> fallback_;
float scale_;
bool needs_mask_;
bool quantized_;
int group_size_;
int bits_;
};
class AffineQuantize : public Custom {

39
python/src/fast.cpp
View File

@@ -161,6 +161,45 @@ void init_fast(nb::module_& parent_module) {
array: The output array.
)pbdoc");
m.def(
"quantized_scaled_dot_product_attention",
&fast::quantized_scaled_dot_product_attention,
"q"_a,
"k"_a,
"k_scales"_a,
"k_biases"_a,
"v"_a,
"v_scales"_a,
"v_biases"_a,
nb::kw_only(),
"scale"_a,
"mask"_a = nb::none(),
"group_size"_a = 64,
"bits"_a = 4,
"stream"_a = nb::none(),
nb::sig(
"def quantized_scaled_dot_product_attention(q: array, k: array, k_scales: array, k_biases: array, v: array, v_scales: array, v_biases: array, *, scale: float, mask: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"),
R"pbdoc(
A fast implementation of multi-head attention where the keys and values are quantized.
see :func:`scaled_dot_product_attention` for more details.
Args:
q (array): Input query array.
k (array): Input keys array.
k_scales (array): Scales for the quantized keys array.
k_biases (array): Biases for the quantized keys array.
v (array): Input values array.
v_scales (array): Scales for the quantized values array.
v_biases (array): Biases for the quantized values array.
scale (float): Scale for queries (typically ``1.0 / sqrt(q.shape(-1)``)
mask (array, optional): An additive mask to apply to the query-key scores.
group_size (int): The group size used in the KV quantization.
bits (int): The bits used in the KV quantization.
Returns:
array: The output array.
)pbdoc");
m.def(
"metal_kernel",
[](const std::string& name,