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Allow different value dimensions in sdpa_vector (#1811)
This commit is contained in:
Angelos Katharopoulos
GitHub
parent
b7c9f1d38f
commit
f5cc1eea72
@ -8,14 +8,23 @@ L = 16384
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H = 32
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H_k = H // 4
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D = 128
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V = 128
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dtype = mx.float16
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loops = 10
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def attention(q, k, v, mask=None):
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def upproject(x, w):
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if w is None:
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return x
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else:
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return x @ w.T
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def attention(q, k, v, mask=None, w=None):
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def _sdpa(q, k, v):
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B, Hq, L, D = q.shape
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_, Hk, S, _ = k.shape
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_, _, _, V = v.shape
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q = q.reshape(B, Hk, Hq // Hk, L, D)
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k = k[:, :, None, :, :]
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v = v[:, :, None, :, :]
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@ -25,16 +34,18 @@ def attention(q, k, v, mask=None):
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s = mx.where(m, s, mx.finfo(s.dtype).min)
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p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
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o = p @ v
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return o.reshape(B, Hq, L, D)
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return o.reshape(B, Hq, L, V)
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for i in range(loops):
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q = _sdpa(q, k, v)
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q = upproject(q, w)
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return q
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def sdpa(q, k, v, mask=None):
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def sdpa(q, k, v, mask=None, w=None):
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for i in range(loops):
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q = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0, mask=mask)
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q = upproject(q, w)
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return q
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@ -42,34 +53,37 @@ def time_self_attention_primitives():
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mx.random.seed(3)
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q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
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k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
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v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
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mx.eval(q, k, v)
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time_fn(attention, q, k, v)
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v = mx.random.uniform(shape=(1, H_k, L, V)).astype(dtype)
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w = mx.random.uniform(shape=(D, V)).astype(dtype) if V != D else None
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mx.eval(q, k, v, w)
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time_fn(attention, q, k, v, w=w)
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def time_self_attention_sdpa():
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mx.random.seed(3)
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q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
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k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
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v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
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mx.eval(q, k, v)
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time_fn(sdpa, q, k, v)
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v = mx.random.uniform(shape=(1, H_k, L, V)).astype(dtype)
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w = mx.random.uniform(shape=(D, V)).astype(dtype) if V != D else None
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mx.eval(q, k, v, w)
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time_fn(sdpa, q, k, v, w=w)
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def time_self_attention_sdpa_with_mask():
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mx.random.seed(3)
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q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
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k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
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v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
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v = mx.random.uniform(shape=(1, H_k, L, V)).astype(dtype)
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w = mx.random.uniform(shape=(D, V)).astype(dtype) if V != D else None
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mask = mx.full((L,), True)
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mask[L // 2 :] = False
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mx.eval(q, k, v, mask)
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mx.eval(q, k, v, mask, w)
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def sdpa_mask(*args):
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return sdpa(*args, mask=mask)
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return sdpa(*args, mask=mask, w=w)
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def attention_mask(*args):
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return attention(*args, mask=mask)
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return attention(*args, mask=mask, w=w)
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time_fn(attention_mask, q, k, v)
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time_fn(sdpa_mask, q, k, v)
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@ -7,15 +7,34 @@ using namespace metal;
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// clang-format off
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// SDPA vector instantiations
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#define instantiate_sdpa_vector(type, head_dim) \
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instantiate_kernel("sdpa_vector_" #type "_" #head_dim, sdpa_vector, type, head_dim) \
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instantiate_kernel("sdpa_vector_2pass_1_" #type "_" #head_dim, sdpa_vector_2pass_1, type, head_dim) \
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instantiate_kernel("sdpa_vector_2pass_2_" #type "_" #head_dim, sdpa_vector_2pass_2, type, head_dim)
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#define instantiate_sdpa_vector_aggregation(type, value_dim) \
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instantiate_kernel( \
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"sdpa_vector_2pass_2_" #type "_" #value_dim, \
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sdpa_vector_2pass_2, \
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type, \
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value_dim)
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#define instantiate_sdpa_vector_heads(type) \
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instantiate_sdpa_vector(type, 64) \
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instantiate_sdpa_vector(type, 96) \
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instantiate_sdpa_vector(type, 128)
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#define instantiate_sdpa_vector(type, qk_dim, value_dim) \
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instantiate_kernel( \
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"sdpa_vector_" #type "_" #qk_dim "_" #value_dim, \
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sdpa_vector, \
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type, \
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qk_dim, \
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value_dim) \
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instantiate_kernel( \
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"sdpa_vector_2pass_1_" #type "_" #qk_dim "_" #value_dim, \
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sdpa_vector_2pass_1, \
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type, \
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qk_dim, \
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value_dim)
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#define instantiate_sdpa_vector_heads(type) \
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instantiate_sdpa_vector(type, 64, 64) \
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instantiate_sdpa_vector(type, 96, 96) \
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instantiate_sdpa_vector(type, 128, 128) \
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instantiate_sdpa_vector_aggregation(type, 64) \
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instantiate_sdpa_vector_aggregation(type, 96) \
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instantiate_sdpa_vector_aggregation(type, 128)
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instantiate_sdpa_vector_heads(float)
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instantiate_sdpa_vector_heads(bfloat16_t)
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@ -6,7 +6,7 @@ using namespace metal;
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constant bool has_mask [[function_constant(20)]];
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template <typename T, int D>
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template <typename T, int D, int V = D>
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[[kernel]] void sdpa_vector(
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const device T* queries [[buffer(0)]],
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const device T* keys [[buffer(1)]],
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@ -25,14 +25,16 @@ template <typename T, int D>
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uint simd_lid [[thread_index_in_simdgroup]]) {
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constexpr int BN = 32;
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constexpr int BD = 32;
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constexpr int elem_per_thread = D / BD;
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constexpr int stride = BN * D;
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constexpr int qk_per_thread = D / BD;
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constexpr int v_per_thread = V / BD;
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constexpr int inner_k_stride = BN * D;
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constexpr int inner_v_stride = BN * V;
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typedef float U;
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thread U q[elem_per_thread];
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thread U k[elem_per_thread];
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thread U o[elem_per_thread];
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thread U q[qk_per_thread];
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thread U k[qk_per_thread];
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thread U o[v_per_thread];
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threadgroup U outputs[BN * BD];
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threadgroup U max_scores[BN];
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@ -41,19 +43,19 @@ template <typename T, int D>
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// Adjust positions
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const int head_idx = tid.y;
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const int kv_head_idx = head_idx / gqa_factor;
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queries += head_idx * D + simd_lid * elem_per_thread;
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keys += kv_head_idx * k_stride + simd_gid * D + simd_lid * elem_per_thread;
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values += kv_head_idx * v_stride + simd_gid * D + simd_lid * elem_per_thread;
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queries += head_idx * D + simd_lid * qk_per_thread;
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keys += kv_head_idx * k_stride + simd_gid * D + simd_lid * qk_per_thread;
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values += kv_head_idx * v_stride + simd_gid * V + simd_lid * v_per_thread;
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if (has_mask) {
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mask += head_idx * mask_head_stride + simd_gid * mask_seq_stride;
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}
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out += head_idx * D + simd_gid * elem_per_thread;
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out += head_idx * V + simd_gid * v_per_thread;
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// Read the query and 0 the output accumulator
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < qk_per_thread; i++) {
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q[i] = static_cast<U>(scale) * queries[i];
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}
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < v_per_thread; i++) {
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o[i] = 0;
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}
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@ -64,13 +66,13 @@ template <typename T, int D>
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for (int i = simd_gid; i < N; i += BN) {
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if (!has_mask || mask[0]) {
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// Read the key
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for (int j = 0; j < elem_per_thread; j++) {
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for (int j = 0; j < qk_per_thread; j++) {
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k[j] = keys[j];
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}
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// Compute the i-th score
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U score = 0;
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for (int j = 0; j < elem_per_thread; j++) {
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for (int j = 0; j < qk_per_thread; j++) {
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score += q[j] * k[j];
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}
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score = simd_sum(score);
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@ -84,14 +86,14 @@ template <typename T, int D>
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sum_exp_score = sum_exp_score * factor + exp_score;
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// Update the output accumulator
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for (int j = 0; j < elem_per_thread; j++) {
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for (int j = 0; j < v_per_thread; j++) {
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o[j] = o[j] * factor + exp_score * values[j];
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}
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}
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// Move the pointers to the next kv
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keys += stride;
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values += stride;
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keys += inner_k_stride;
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values += inner_v_stride;
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if (has_mask) {
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mask += BN * mask_seq_stride;
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}
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@ -111,7 +113,7 @@ template <typename T, int D>
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sum_exp_score = simd_sum(sum_exp_scores[simd_lid] * factor);
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// Now we need to aggregate all the outputs
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < v_per_thread; i++) {
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outputs[simd_lid * BD + simd_gid] = o[i];
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threadgroup_barrier(mem_flags::mem_threadgroup);
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o[i] = simd_sum(outputs[simd_gid * BD + simd_lid] * factor) / sum_exp_score;
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@ -120,13 +122,13 @@ template <typename T, int D>
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// And write the output
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if (simd_lid == 0) {
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < v_per_thread; i++) {
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out[i] = static_cast<T>(o[i]);
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}
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}
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}
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template <typename T, int D>
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template <typename T, int D, int V = D>
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[[kernel]] void sdpa_vector_2pass_1(
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const device T* queries [[buffer(0)]],
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const device T* keys [[buffer(1)]],
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@ -147,15 +149,17 @@ template <typename T, int D>
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uint simd_lid [[thread_index_in_simdgroup]]) {
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constexpr int BN = 8;
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constexpr int BD = 32;
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constexpr int elem_per_thread = D / BD;
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constexpr int stride = BN * D;
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constexpr int qk_per_thread = D / BD;
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constexpr int v_per_thread = V / BD;
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constexpr int inner_k_stride = BN * D;
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constexpr int inner_v_stride = BN * V;
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constexpr int blocks = 32;
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typedef float U;
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thread U q[elem_per_thread];
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thread U k[elem_per_thread];
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thread U o[elem_per_thread];
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thread U q[qk_per_thread];
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thread U k[qk_per_thread];
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thread U o[v_per_thread];
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threadgroup U outputs[BN * BD];
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threadgroup U max_scores[BN];
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@ -165,12 +169,12 @@ template <typename T, int D>
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const int block_idx = tid.z;
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const int head_idx = tid.y;
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const int kv_head_idx = head_idx / gqa_factor;
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queries += head_idx * D + simd_lid * elem_per_thread;
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queries += head_idx * D + simd_lid * qk_per_thread;
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keys += kv_head_idx * k_stride + (block_idx * BN + simd_gid) * D +
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simd_lid * elem_per_thread;
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values += kv_head_idx * v_stride + (block_idx * BN + simd_gid) * D +
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simd_lid * elem_per_thread;
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out += head_idx * blocks * D + block_idx * D + simd_lid * elem_per_thread;
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simd_lid * qk_per_thread;
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values += kv_head_idx * v_stride + (block_idx * BN + simd_gid) * V +
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simd_lid * v_per_thread;
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out += head_idx * blocks * V + block_idx * V + simd_lid * v_per_thread;
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if (has_mask) {
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mask += head_idx * mask_head_stride +
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(block_idx * BN + simd_gid) * mask_seq_stride;
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@ -179,10 +183,10 @@ template <typename T, int D>
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maxs += head_idx * blocks + block_idx;
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// Read the query and 0 the output accumulator
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < qk_per_thread; i++) {
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q[i] = static_cast<U>(scale) * queries[i];
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}
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < v_per_thread; i++) {
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o[i] = 0;
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}
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@ -193,13 +197,13 @@ template <typename T, int D>
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for (int i = block_idx * BN + simd_gid; i < N; i += blocks * BN) {
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if (!has_mask || mask[0]) {
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// Read the key
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < qk_per_thread; i++) {
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k[i] = keys[i];
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}
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// Compute the i-th score
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U score = 0;
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < qk_per_thread; i++) {
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score += q[i] * k[i];
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}
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score = simd_sum(score);
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@ -213,14 +217,14 @@ template <typename T, int D>
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sum_exp_score = sum_exp_score * factor + exp_score;
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// Update the output accumulator
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < v_per_thread; i++) {
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o[i] = o[i] * factor + exp_score * values[i];
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}
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}
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// Move the pointers to the next kv
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keys += blocks * stride;
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values += blocks * stride;
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keys += blocks * inner_k_stride;
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values += blocks * inner_v_stride;
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if (has_mask) {
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mask += BN * blocks * mask_seq_stride;
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}
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@ -247,7 +251,7 @@ template <typename T, int D>
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}
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// Now we need to aggregate all the outputs
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for (int i = 0; i < elem_per_thread; i++) {
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for (int i = 0; i < v_per_thread; i++) {
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outputs[simd_lid * BN + simd_gid] =
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o[i] * fast::exp(max_scores[simd_gid] - new_max);
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threadgroup_barrier(mem_flags::mem_threadgroup);
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@ -124,6 +124,8 @@ void sdpa_vector(
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kname += get_type_string(q.dtype());
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kname += "_";
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kname += std::to_string(q.shape(-1));
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kname += "_";
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kname += std::to_string(v.shape(-1));
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// Compute the necessary sizes
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int gqa_factor = q.shape(1) / k.shape(1);
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@ -185,6 +187,8 @@ void sdpa_vector_2pass(
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kname += get_type_string(q.dtype());
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kname += "_";
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kname += std::to_string(q.shape(-1));
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kname += "_";
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kname += std::to_string(v.shape(-1));
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// Compute the necessary sizes
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int gqa_factor = q.shape(1) / k.shape(1);
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@ -256,7 +260,7 @@ void sdpa_vector_2pass(
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kname += "sdpa_vector_2pass_2_";
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kname += get_type_string(q.dtype());
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kname += "_";
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kname += std::to_string(q.shape(-1));
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kname += std::to_string(v.shape(-1));
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// Get the kernel
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kernel = d.get_kernel(kname);
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@ -332,7 +336,7 @@ void ScaledDotProductAttention::eval_gpu(
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const auto& v = copy_unless(is_contiguous_except_seq_len, v_pre);
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// Donate the query if possible
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if (q.is_donatable()) {
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if (q.is_donatable() && q.size() == o.size()) {
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o.move_shared_buffer(q);
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} else {
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o.set_data(allocator::malloc_or_wait(o.nbytes()));
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19
mlx/fast.cpp
19
mlx/fast.cpp
@ -684,23 +684,20 @@ array scaled_dot_product_attention(
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const size_t query_head_dim = q.shape(-1);
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const size_t query_sequence_length = q.shape(2);
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bool implementation_supports_use_case = query_head_dim == value_head_dim;
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const bool sdpa_vector_supported_head_dim =
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query_head_dim == 64 || query_head_dim == 96 || query_head_dim == 128;
|
||||
const bool sdpa_full_supported_head_dim =
|
||||
query_head_dim == 64 || query_head_dim == 80;
|
||||
query_head_dim == value_head_dim &&
|
||||
(query_head_dim == 64 || query_head_dim == 96 || query_head_dim == 128);
|
||||
const bool sdpa_full_supported_head_dim = query_head_dim == value_head_dim &&
|
||||
(query_head_dim == 64 || query_head_dim == 80);
|
||||
|
||||
const bool supports_sdpa_full = query_sequence_length >= threshold &&
|
||||
!mask.has_value() && sdpa_full_supported_head_dim &&
|
||||
stream.device == Device::gpu;
|
||||
const bool supports_sdpa_full = query_sequence_length >= threshold && !mask &&
|
||||
sdpa_full_supported_head_dim && stream.device == Device::gpu;
|
||||
|
||||
const bool supported_mask = !mask || (mask->dtype() == bool_);
|
||||
const bool supports_sdpa_vector = query_sequence_length == 1 &&
|
||||
supported_mask && sdpa_vector_supported_head_dim &&
|
||||
(!mask || mask->dtype() == bool_) && sdpa_vector_supported_head_dim &&
|
||||
stream.device == Device::gpu;
|
||||
|
||||
implementation_supports_use_case &=
|
||||
const bool implementation_supports_use_case =
|
||||
supports_sdpa_full || supports_sdpa_vector;
|
||||
|
||||
std::vector<array> inputs = {q, k, v};
|
||||
|
||||
@ -262,6 +262,23 @@ class TestFastSDPA(mlx_tests.MLXTestCase):
|
||||
)
|
||||
self.assertTrue(mx.allclose(ref, out, atol=1e-4, rtol=1e-4))
|
||||
|
||||
@unittest.skip("Different head and value dims is not enabled")
|
||||
def test_fast_sdpa_vector_value_dims(self):
|
||||
D = 192
|
||||
V = 128
|
||||
Nq = 4
|
||||
Nkv = 1
|
||||
scale = 1.0
|
||||
mx.random.seed(0)
|
||||
|
||||
for L in [43, 128, 237, 8192]:
|
||||
q = 5e-1 * mx.random.normal(shape=(1, Nq, 1, D))
|
||||
k = 5e-1 * mx.random.normal(shape=(1, Nkv, L, D))
|
||||
v = 5e-1 * mx.random.normal(shape=(1, Nkv, L, V))
|
||||
ref = mlx_primitives_sdpa(q, k, v, scale)
|
||||
out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale)
|
||||
self.assertTrue(mx.allclose(ref, out, atol=1e-4, rtol=1e-4))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main(failfast=True)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user