File size: 4,951 Bytes
440e354 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
from functools import partial
from time import time
import os
import numpy as np
import jax
import jax.flatten_util
import jax.numpy as jnp
import mlxu
from EasyLM.bpt import blockwise_attn
from EasyLM.jax_utils import (
get_float_dtype_by_name, set_random_seed, next_rng, JaxRNG
)
FLAGS, _ = mlxu.define_flags_with_default(
seed=42,
dtype='fp32',
embed_dim=2048,
n_heads=16,
ref_attn_seq_len=2048,
eff_attn_seq_len=16384,
batch_size=1,
query_chunk_size=2048,
key_chunk_size=2048,
warmup_steps=40,
steps=200,
)
def main(argv):
def random_kqv(rng_key, seq_len):
rng_generator = JaxRNG(rng_key)
kqv = []
for i in range(3):
kqv.append(
jax.random.normal(
rng_generator(),
(FLAGS.batch_size, seq_len, FLAGS.n_heads, FLAGS.embed_dim // FLAGS.n_heads),
dtype=get_float_dtype_by_name(FLAGS.dtype)
)
)
return tuple(kqv)
def reference_attn(query, key, value):
dtype = get_float_dtype_by_name(FLAGS.dtype)
query = query / jnp.sqrt(query.shape[-1]).astype(dtype)
logits = jnp.einsum("bqhc,bkhc->bhqk", query, key)
mask_value = jnp.finfo(logits.dtype).min
_, q_seq_len, _, _ = query.shape
_, kv_seq_len, _, _ = key.shape
mask_shape = (q_seq_len, kv_seq_len)
row_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 0)
col_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 1)
causal_mask = (row_ids < col_ids)[None, None, :, :]
logits = logits + jnp.where(causal_mask, mask_value, 0.0)
weights = jax.nn.softmax(logits, axis=-1)
out = jnp.einsum("bhqk,bkhc->bqhc", weights, value)
return out
def efficient_attention(query, key, value):
dtype = get_float_dtype_by_name(FLAGS.dtype)
return blockwise_attn(
query, key, value,
bias=None,
deterministic=True,
dropout_rng=None,
attn_pdrop=0.0,
causal=True,
query_chunk_size=FLAGS.query_chunk_size,
key_chunk_size=FLAGS.key_chunk_size,
dtype=get_float_dtype_by_name(FLAGS.dtype),
policy=jax.checkpoint_policies.nothing_saveable(),
precision=None,
float32_logits=True,
prevent_cse=True,
)
@partial(jax.jit, static_argnums=(1,))
def reference_attn_forward_backward(rng_key, seq_len):
@partial(jax.grad, argnums=(0, 1, 2))
@partial(jax.checkpoint, policy=jax.checkpoint_policies.nothing_saveable())
def grad_fn(query, key, value):
out = reference_attn(query, key, value)
return jnp.mean(out)
query, key, value = random_kqv(rng_key, seq_len)
return jax.flatten_util.ravel_pytree(
grad_fn(query, key, value)[1]
)[0].mean()
@partial(jax.jit, static_argnums=(1,))
def efficient_attn_forward_backward(rng_key, seq_len):
@partial(jax.grad, argnums=(0, 1, 2))
def grad_fn(query, key, value):
out = efficient_attention(query, key, value)
return jnp.mean(out)
query, key, value = random_kqv(rng_key, seq_len)
return jax.flatten_util.ravel_pytree(
grad_fn(query, key, value)[1]
)[0].mean()
set_random_seed(FLAGS.seed)
jax.block_until_ready(reference_attn_forward_backward(next_rng(), FLAGS.ref_attn_seq_len))
jax.block_until_ready(efficient_attn_forward_backward(next_rng(), FLAGS.eff_attn_seq_len))
all_results = []
for i in range(FLAGS.warmup_steps):
all_results.append(reference_attn_forward_backward(next_rng(), FLAGS.ref_attn_seq_len))
jax.block_until_ready(all_results)
start_time = time()
all_results = []
for i in range(FLAGS.steps):
all_results.append(reference_attn_forward_backward(next_rng(), FLAGS.ref_attn_seq_len))
jax.block_until_ready(all_results)
elapsed_time_ref_attn = time() - start_time
print(f'Reference attention: {elapsed_time_ref_attn:.3f} seconds')
all_results = []
for i in range(FLAGS.warmup_steps):
all_results.append(efficient_attn_forward_backward(next_rng(), FLAGS.eff_attn_seq_len))
jax.block_until_ready(all_results)
start_time = time()
all_results = []
for i in range(FLAGS.steps):
all_results.append(efficient_attn_forward_backward(next_rng(), FLAGS.eff_attn_seq_len))
jax.block_until_ready(all_results)
elapsed_time_efficient_attn = time() - start_time
print(f'Efficient attention: {elapsed_time_efficient_attn:.3f} seconds')
flops_ratio = (FLAGS.eff_attn_seq_len / FLAGS.ref_attn_seq_len) ** 2
efficiency = elapsed_time_ref_attn / elapsed_time_efficient_attn * flops_ratio
print(f'Efficiency: {efficiency:.3f}')
if __name__ == '__main__':
mlxu.run(main)
|