Update sdpa_benchmarks

benchmarks/python/sdpa_bench.py

@@ -1,62 +1,173 @@
+# Copyright © 2024 Apple Inc.
+
 import argparse
 import math
+import os
+import subprocess
+import time
 
 import mlx.core as mx
-from time_utils import time_fn
+import numpy as np
 
-MAX_SEQ = 300
-START_SEQ = 100
-SEQ_INCREMENT = 50
+device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
+device_name = device_name.decode("utf-8").strip("\n")
+
+N_warmup = 5
+N_iter_bench = 40
+N_iter_func = 8
 
 
-def time_self_attention_primitives():
-    mx.random.seed(3)
-    B = 2
-    H = 38
-    D = 64
-    for R in range(START_SEQ, MAX_SEQ, SEQ_INCREMENT):
-        q = mx.random.uniform(shape=(B, H, R, D))
-        k = mx.random.uniform(shape=(B, H, R, D))
-        v = mx.random.uniform(shape=(B, H, R, D))
-        scale = 1.0 / math.sqrt(float(D))
-        mx.eval(q, k, v)
+def bench(f, *args):
+    for i in range(N_warmup):
+        f(*args)
 
-        def sdpa_primitives(qs, ks, vs, alpha):
-            s = (alpha * qs) @ ks.transpose(0, 1, 3, 2)
-            p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
-            o = p @ vs
-            return o
-
-        time_fn(sdpa_primitives, q, k, v, scale)
+    s = time.perf_counter_ns()
+    for i in range(N_iter_bench):
+        f(*args)
+    e = time.perf_counter_ns()
+    return (e - s) * 1e-9
 
 
-def time_self_attention_sdpa():
-    mx.random.seed(3)
-    B = 2
-    H = 38
-    D = 64
-    for R in range(START_SEQ, MAX_SEQ, SEQ_INCREMENT):
-        q = mx.random.uniform(shape=(B, H, R, D))
-        k = mx.random.uniform(shape=(B, H, R, D))
-        v = mx.random.uniform(shape=(B, H, R, D))
-        scale = 1.0 / math.sqrt(float(D))
-        mx.eval(q, k, v)
+def mlx_sdpa_fused_inner(q, k, v, scale):
+    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=None)
 
-        def sdpa_fused(qs, ks, vs, alpha):
-            o = mx.fast.scaled_dot_product_attention(qs, ks, vs, scale=alpha)
-            return o
 
-        time_fn(sdpa_fused, q, k, v, scale)
+def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
+    q_dtype = q.dtype
+    q = q * mx.array(scale, q_dtype)
+    n_q_heads = q.shape[-3]
+    n_kv_heads = k.shape[-3]
+    n_repeats = n_q_heads // n_kv_heads
+
+    B = q.shape[0]
+    L = q.shape[2]
+
+    if n_repeats > 1:
+        q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1])
+        k = mx.expand_dims(k, 2)
+        v = mx.expand_dims(v, 2)
+
+    scores = q @ mx.swapaxes(k, -1, -2)
+    if f32softmax:
+        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(q_dtype)
+    else:
+        scores = mx.softmax(scores, axis=-1)
+
+    out = scores @ v
+    if n_repeats > 1:
+        out = mx.reshape(out, [B, n_q_heads, L, -1])
+
+    return out
+
+
+def mlx_spda_unfused(q, k, v, scale, transpose):
+    q_out = q
+    if transpose:
+        k = mx.transpose(k, (0, 2, 1, 3))
+        v = mx.transpose(v, (0, 2, 1, 3))
+
+    for i in range(N_iter_func):
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = mlx_sdpa_unfused_inner(q_out, k, v, scale)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+
+    mx.eval(q_out)
+    return q_out
+
+
+def mlx_spda_fused(q, k, v, scale, transpose):
+    q_out = q
+    if transpose:
+        k = mx.transpose(k, (0, 2, 1, 3))
+        v = mx.transpose(v, (0, 2, 1, 3))
+
+    for i in range(N_iter_func):
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = mlx_sdpa_fused_inner(q_out, k, v, scale)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+
+    mx.eval(q_out)
+    return q_out
+
+
+def bench_shape(B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose=True):
+    shape_q = (
+        (B, qsl, n_q_heads, head_dim) if transpose else (B, n_q_heads, qsl, head_dim)
+    )
+    shape_kv = (
+        (B, ksl, n_kv_heads, head_dim) if transpose else (B, n_kv_heads, ksl, head_dim)
+    )
+
+    q_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_q).astype(np_dtype)
+    k_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
+    v_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
+
+    scale = math.sqrt(1.0 / head_dim)
+
+    q_mx = mx.array(q_np)
+    k_mx = mx.array(k_np)
+    v_mx = mx.array(v_np)
+
+    time_mlx_unfused = bench(mlx_spda_unfused, q_mx, k_mx, v_mx, scale, transpose)
+    time_mlx_fused = bench(mlx_spda_fused, q_mx, k_mx, v_mx, scale, transpose)
+
+    if transpose:
+        q_mx = mx.transpose(q_mx, (0, 2, 1, 3))
+        k_mx = mx.transpose(k_mx, (0, 2, 1, 3))
+        v_mx = mx.transpose(v_mx, (0, 2, 1, 3))
+
+    o_mlx_fused = mlx_sdpa_fused_inner(q_mx, k_mx, v_mx, scale)
+    o_mlx_unfused = mlx_sdpa_unfused_inner(q_mx, k_mx, v_mx, scale, f32softmax=True)
+
+    atol = 1e-5 if np_dtype == np.float32 else 1e-4
+
+    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol):
+        print(
+            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
+        )
+
+    return time_mlx_fused, time_mlx_unfused
+
+
+def get_gflop_count(B, M, N, K):
+    return float(2.0 * N_iter_bench * N_iter_func * B * M * N * K) / float(1024.0**3)
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser("MLX benchmarks.")
-    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
-    args = parser.parse_args()
-    if args.gpu:
-        mx.set_default_device(mx.gpu)
-    else:
-        mx.set_default_device(mx.cpu)
+    parser = argparse.ArgumentParser(description="Run gemm benchmarks")
 
-    time_self_attention_sdpa()
-    time_self_attention_primitives()
+    dtypes = (
+        "float16",
+        "float32",
+    )
+    transposes = (False,)
+    shapes = (
+        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
+        (1, 32, 32, 64, 32, 32),
+        (1, 64, 64, 64, 32, 32),
+        (1, 128, 128, 64, 32, 32),
+        (1, 256, 256, 64, 32, 32),
+        (1, 512, 512, 64, 32, 32),
+        (1, 1024, 1024, 64, 32, 32),
+        (1, 2048, 2048, 64, 32, 32),
+        (1, 4096, 4096, 64, 32, 32),
+    )
+
+    print("  B,   qsl,   ksl, hdim, n_qh, n_kvh, tpose,   dtype, t_unfs, t_fuse, diff%")
+
+    for dtype in dtypes:
+        for transpose in transposes:
+            for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes:
+                np_dtype = getattr(np, dtype)
+                time_mlx_fused, time_mlx_unfused = bench_shape(
+                    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose
+                )
+                diff = time_mlx_unfused / time_mlx_fused - 1.0
+                t_str = 1 if transpose else 0
+                print(
+                    f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:5d}, {dtype}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
+                )