working qsdpa

benchmarks/python/sdpa_vector_bench.py

@@ -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)