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cb49561 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | import numpy as np
def _rmsnorm(x, w, eps, out, sq):
np.square(x, out=sq)
ss = np.mean(sq, axis=-1, keepdims=True)
np.multiply(x, np.reciprocal(np.sqrt(ss + np.float32(eps))), out=out)
np.multiply(out, w, out=out)
return out
def _rope(t, cos, sin, out):
"""Rotary embedding over the last axis, applied per head. t: (H, T, head_dim)."""
half = t.shape[-1] // 2
a = t[..., :half]
b = t[..., half:]
np.subtract(a * cos, b * sin, out=out[..., :half])
np.add(a * sin, b * cos, out=out[..., half:])
return out
class _Executor:
def __init__(self, spec, weights, n_workers):
self.spec = spec
self.w = weights
self.n_workers = int(n_workers)
T = spec["T"]
d = spec["d_model"]
d_ff = spec["d_ff"]
hd = spec["head_dim"]
H = spec["n_heads"]
KV = spec["n_kv_heads"]
self.eps = float(spec["rms_eps"])
self.scale = np.float32(hd ** -0.5)
self.rep = H // KV
self.buf = {
"h": np.empty((T, d), dtype=np.float32),
"n1": np.empty((T, d), dtype=np.float32),
"n2": np.empty((T, d), dtype=np.float32),
"q": np.empty((T, H * hd), dtype=np.float32),
"k": np.empty((T, KV * hd), dtype=np.float32),
"v": np.empty((T, KV * hd), dtype=np.float32),
"att": np.empty((T, H * hd), dtype=np.float32),
"ao": np.empty((T, d), dtype=np.float32),
"g": np.empty((T, d_ff), dtype=np.float32),
"u": np.empty((T, d_ff), dtype=np.float32),
"f": np.empty((T, d_ff), dtype=np.float32),
"fo": np.empty((T, d), dtype=np.float32),
}
self.qr = np.empty((H, T, hd), dtype=np.float32)
self.kr = np.empty((KV, T, hd), dtype=np.float32)
self.kkT = np.empty((H, hd, T), dtype=np.float32)
self.vv = np.empty((H, T, hd), dtype=np.float32)
self.scores = np.empty((H, T, T), dtype=np.float32)
self.probs = np.empty((H, T, T), dtype=np.float32)
self.ctx = np.empty((H, T, hd), dtype=np.float32)
self.sq = np.empty((T, d), dtype=np.float32)
self.cos = weights["rope_cos"]
self.sin = weights["rope_sin"]
self.mask = weights["attn_mask"]
self.plan = []
for layer in spec["layers"]:
ops = []
for node in layer:
w = weights[node["weight"]] if node["weight"] else None
ops.append((node["op"], node["out"], node["inputs"], w))
self.plan.append(ops)
self.final_w = weights[spec["final"]["weight"]]
def _attention(self, q, k, v, out):
H, T, hd = self.qr.shape
KV = self.kr.shape[0]
_rope(q.reshape(T, H, hd).transpose(1, 0, 2), self.cos, self.sin, self.qr)
_rope(k.reshape(T, KV, hd).transpose(1, 0, 2), self.cos, self.sin, self.kr)
self.kkT.reshape(KV, self.rep, hd, T)[...] = self.kr.transpose(0, 2, 1)[:, None]
self.vv.reshape(KV, self.rep, T, hd)[...] = \
v.reshape(T, KV, hd).transpose(1, 0, 2)[:, None]
np.matmul(self.qr, self.kkT, out=self.scores)
np.multiply(self.scores, self.scale, out=self.scores)
np.add(self.scores, self.mask, out=self.scores)
np.subtract(self.scores, np.max(self.scores, axis=-1, keepdims=True),
out=self.probs)
np.exp(self.probs, out=self.probs)
np.divide(self.probs, np.sum(self.probs, axis=-1, keepdims=True), out=self.probs)
np.matmul(self.probs, self.vv, out=self.ctx)
out.reshape(T, H, hd)[...] = self.ctx.transpose(1, 0, 2)
return out
def __call__(self, x):
buf = self.buf
h = buf["h"]
h[...] = x
for ops in self.plan:
for op, out_name, inp, W in ops:
if op == "matmul":
np.matmul(buf[inp[0]], W, out=buf[out_name])
elif op == "rmsnorm":
_rmsnorm(buf[inp[0]], W, self.eps, buf[out_name], self.sq)
elif op == "attention":
self._attention(buf[inp[0]], buf[inp[1]], buf[inp[2]], buf[out_name])
elif op == "add":
np.add(buf[inp[0]], buf[inp[1]], out=buf[out_name])
elif op == "swiglu":
g = buf[inp[0]]
f = buf[out_name]
np.negative(g, out=f)
np.exp(f, out=f)
np.add(f, np.float32(1.0), out=f)
np.divide(g, f, out=f)
np.multiply(f, buf[inp[1]], out=f)
else:
raise ValueError("unknown op %r" % op)
return _rmsnorm(h, self.final_w, self.eps, buf["n1"], self.sq)
def build_executor(graph_spec, weights, n_workers):
"""Build an executor for `graph_spec`. See the module docstring for the contract."""
return _Executor(graph_spec, weights, n_workers)
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