clean

benchmarks/python/sdpa_vector_bench.py

@@ -1,18 +1,15 @@
 import mlx.core as mx
 import numpy as np
+from mlx.utils import tree_map
 from time_utils import time_fn
 
-L = 16
+L = 65536
 H = 32
 H_k = 32 // 4
 D = 128
 
 
 def attention(q, k, v):
-    k = mx.quantize(k)
-    v = mx.quantize(v)
-    k = mx.dequantize(*k)
-    v = mx.dequantize(*v)
     B, Hq, L, D = q.shape
     _, Hk, S, _ = k.shape
     q = q.reshape(B, Hk, Hq // Hk, L, D)
@@ -25,21 +22,31 @@ def attention(q, k, v):
 
 
 def sdpa(q, k, v):
-    k = mx.quantize(k, bits=8)
-    v = mx.quantize(v, bits=8)
-    k = mx.dequantize(*k, bits=8)
-    v = mx.dequantize(*v, bits=8)
     return mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0, mask=None)
 
 
 def quant_sdpa(q, k, v):
-    k = mx.quantize(k, bits=8)
-    v = mx.quantize(v, bits=8)
     return mx.fast.quantized_scaled_dot_product_attention(
         q, *k, *v, scale=1.0, mask=None, bits=8
     )
 
 
+def quant_attention(q, k, v):
+    B, Hq, L, D = q.shape
+    Hk = k[0].shape[1]
+
+    q = q.reshape((B, Hk, Hq // Hk, L, D))
+    k = tree_map(lambda x: mx.expand_dims(x, axis=2), k)
+    v = tree_map(lambda x: mx.expand_dims(x, axis=2), v)
+
+    scores = mx.quantized_matmul(q, *k, transpose=True)
+    scores = mx.softmax(scores, axis=-1)
+
+    out = mx.quantized_matmul(scores, *v, transpose=False)
+    out = out.reshape((B, Hq, L, D))
+    return out
+
+
 def time_self_attention_primitives(q, k, v):
     time_fn(attention, q, k, v)
 
@@ -52,34 +59,22 @@ def time_self_attention_quant_sdpa(q, k, v):
     time_fn(quant_sdpa, q, k, v)
 
 
+def time_self_attention_quant_primitives(q, k, v):
+    time_fn(quant_attention, q, k, v)
+
+
 if __name__ == "__main__":
     mx.random.seed(3)
-    # q = mx.random.uniform(shape=(1, H, 1, D))
-    # k = mx.random.uniform(shape=(1, H_k, L, D))
-    # v = mx.random.uniform(shape=(1, H_k, L, D))
-    q = mx.array(np.load("/Users/alexbarron/mlx-examples/llms/queries.npy"))
-    k = mx.array(np.load("/Users/alexbarron/mlx-examples/llms/keys.npy"))
-    v = mx.array(np.load("/Users/alexbarron/mlx-examples/llms/values.npy"))
-    print(q.dtype)
-    print(q.shape, k.shape, v.shape)
+    q = mx.random.uniform(shape=(1, H, 1, D))
+    k = mx.random.uniform(shape=(1, H_k, L, D))
+    v = mx.random.uniform(shape=(1, H_k, L, D))
     mx.eval(q, k, v)
 
     k_quant = mx.quantize(k)
     v_quant = mx.quantize(v)
     mx.eval(k_quant, v_quant)
 
-    # time_self_attention_sdpa(q, k, v)
-    # time_self_attention_quant_sdpa(q, k_quant, v_quant)
-    # time_self_attention_primitives(q, k, v)
-    q_sdpa = quant_sdpa(q, k, v)
-    print(q_sdpa)
-    # o_attention = attention(q, k, v)
-    # print(o_attention)
-    # np.testing.assert_allclose(q_sdpa, o_attention, atol=1e-5)
-    o_sdpa = sdpa(q, k, v)
-    print(o_sdpa)
-    np.testing.assert_allclose(q_sdpa, o_sdpa, atol=1e-5)
-    # print(o_sdpa[..., :64])
-    # print()
-    # print(o_attention[..., :64])
-    # np.testing.assert_allclose(o_sdpa, o_attention)
+    time_self_attention_sdpa(q, k, v)
+    time_self_attention_quant_sdpa(q, k_quant, v_quant)
+    time_self_attention_primitives(q, k, v)
+    time_self_attention_quant_primitives(q, k_quant, v_quant)

mlx/backend/metal/scaled_dot_product_attention.cpp

@@ -9,8 +9,6 @@
 #include "mlx/backend/metal/kernels/scaled_dot_product_attention_params.h"
 #include "mlx/fast_primitives.h"
 
-#include <iostream>
-
 namespace mlx::core::fast {
 
 namespace {

mlx/fast.cpp

@@ -10,8 +10,6 @@
 #include "mlx/ops.h"
 #include "mlx/transforms.h"
 
-#include <iostream>
-
 namespace mlx::core::fast {
 
 std::vector<array> Custom::vjp(

python/src/fast.cpp

@@ -169,26 +169,22 @@ void init_fast(nb::module_& parent_module) {
       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: ``O = softmax(Q @ K.T, dim=-1) @ V``.
-
-        Supports:
-
-        * `Multi-Head Attention <https://arxiv.org/abs/1706.03762>`_
-        * `Grouped Query Attention <https://arxiv.org/abs/2305.13245>`_
-        * `Multi-Query Attention <https://arxiv.org/abs/1911.02150>`_
-
-        Note: The softmax operation is performed in ``float32`` regardless of
-        the input precision.
+        A fast implementation of multi-head attention where the keys and values are quantized.
 
-        Note: For Grouped Query Attention and Multi-Query Attention, the ``k``
-        and ``v`` inputs should not be pre-tiled to match ``q``.
+        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");