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KoboldAI-Client
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Merge branch 'united' into world-info

This commit is contained in:
Gnome Ann 2021-12-05 13:06:32 -05:00
parent 6d8517e224 c36bc376c0
commit 683bcb824f
3 changed files with 90 additions and 69 deletions
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25
aiserver.py
View File

@ -1838,12 +1838,25 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
raise ValueError("Dynamic world info scanning is not supported by the TPU backend yet")
soft_tokens = None
if(vars.sp is not None):
soft_tokens = np.arange(
tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"],
tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"] + vars.sp_length,
dtype=np.uint32
if(vars.sp is None):
global np
if 'np' not in globals():
import numpy as np
tensor = np.zeros((1, tpu_mtj_backend.params["d_model"]), dtype=np.float32)
rows = tensor.shape[0]
padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows
tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
tensor = tensor.reshape(
tpu_mtj_backend.params["cores_per_replica"],
-1,
tpu_mtj_backend.params["d_model"],
)
vars.sp = tensor
soft_tokens = np.arange(
tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"],
tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"] + vars.sp_length,
dtype=np.uint32
)
genout = tpu_mtj_backend.infer(
txt,
@ -2806,7 +2819,7 @@ def spRequest(filename):
if(vars.model in ("TPUMeshTransformerGPTJ",)):
rows = tensor.shape[0]
padding_amount = -(rows % -tpu_mtj_backend.params["cores_per_replica"])
padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows
tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
tensor = tensor.reshape(
tpu_mtj_backend.params["cores_per_replica"],

4
requirements_mtj.txt
View File

@ -4,11 +4,11 @@ requests
optax >= 0.0.5, <= 0.0.9
dm-haiku
ray[default]
jax == 0.2.12
jax == 0.2.21
transformers
progressbar2
git+https://github.com/VE-FORBRYDERNE/mesh-transformer-jax@ck
flask
Flask-SocketIO
flask-cloudflared >= 0.0.5
flask-ngrok
flask-ngrok

130
tpu_mtj_backend.py
View File

@ -30,7 +30,7 @@ def show_spinner():
def apply_repetition_penalty(logits, tokens, repetition_penalty):
'''
This gets called by generate_scan_fn to apply repetition penalty
This gets called by generate_loop_fn to apply repetition penalty
to the 1D array logits using the provided 1D array of tokens to penalize
'''
# Make a new array with the same length as the tokens array but with
@ -52,9 +52,9 @@ def apply_repetition_penalty(logits, tokens, repetition_penalty):
# positions in the logits array
return logits.at[tokens].set(penalty_logits)
def kobold_sample(key, logits, _, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
def kobold_sample(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
'''
This gets called by generate_scan_fn to apply a series of 4 filters
This gets called by generate_loop_fn to apply a series of 4 filters
to the logits (top-k, then top-p, then TFS, then temperature) before
picking one token using the modified logits
'''
@ -147,7 +147,7 @@ def kobold_sample(key, logits, _, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
# Finally, pick one token using the softmax thingy again (it gives
# an array whose elements sum to 1 so it can be used nicely as a
# probability distribution)
return jax.random.categorical(key, logits, -1).astype(jnp.uint32)[jnp.newaxis], None
return jax.random.categorical(key, logits, -1).astype(jnp.uint32)[jnp.newaxis]
pad_token_id = 50256
@ -155,27 +155,34 @@ class PenalizingCausalTransformer(CausalTransformer):
def __init__(self, config):
# Initialize
super().__init__(config)
def generate(state, key, ctx, ctx_length, aux, sampler_options, soft_embeddings=None):
gen_length = self.gen_length
def generate(state, key, ctx, ctx_length, gen_length, numseqs_aux, sampler_options, soft_embeddings=None):
numseqs = numseqs_aux.shape[0]
# These are the tokens that we don't want the AI to ever write
self.badwords = jnp.array([6880, 50256, 42496, 4613, 17414, 22039, 16410, 27, 29, 38430, 37922, 15913, 24618, 28725, 58, 47175, 36937, 26700, 12878, 16471, 37981, 5218, 29795, 13412, 45160, 3693, 49778, 4211, 20598, 36475, 33409, 44167, 32406, 29847, 29342, 42669, 685, 25787, 7359, 3784, 5320, 33994, 33490, 34516, 43734, 17635, 24293, 9959, 23785, 21737, 28401, 18161, 26358, 32509, 1279, 38155, 18189, 26894, 6927, 14610, 23834, 11037, 14631, 26933, 46904, 22330, 25915, 47934, 38214, 1875, 14692, 41832, 13163, 25970, 29565, 44926, 19841, 37250, 49029, 9609, 44438, 16791, 17816, 30109, 41888, 47527, 42924, 23984, 49074, 33717, 31161, 49082, 30138, 31175, 12240, 14804, 7131, 26076, 33250, 3556, 38381, 36338, 32756, 46581, 17912, 49146])
def generate_sample(context, ctx_length, aux):
def generate_sample(context, ctx_length):
# Give the initial context to the transformer
transformer = CausalTransformerShard(config)
_, initial_state = transformer.generate_initial(context, ctx_length, soft_embeddings=soft_embeddings)
# The "generated" array will contain the tokens from the
# context as well as the tokens picked by the sampler at
# each stage, padded with a bunch of 50256s, so we know
# which tokens have to be repetition penalized
generated = jnp.pad(context, (0, gen_length), constant_values=pad_token_id) # Let it start off with just the 2048 context tokens, plus gen_length 50256s which will be eventually filled with sampler-chosen tokens
generated_index = config["seq"]
# Add that information to generate_scan_fn's starting state
initial_state = (generated, generated_index) + initial_state
def generate_initial_scan_fn(sequence_index, _):
_, initial_state = transformer.generate_initial(context, ctx_length, soft_embeddings=soft_embeddings)
# The "generated" array will contain the tokens from the
# context as well as the tokens picked by the sampler at
# each stage, padded with a bunch of 50256s, so we know
# which tokens have to be repetition penalized
generated = jnp.pad(context, (0, config["seq"]), constant_values=pad_token_id) # Let it start off with just the 2048 context tokens, plus some 50256s which will be eventually filled with sampler-chosen tokens
generated_index = config["seq"]
# Add that information to generate_loop_fn's starting state
initial_state = (generated, generated_index, sequence_index) + initial_state
return sequence_index+1, initial_state
_, initial_states = jax.lax.scan(generate_initial_scan_fn, 0, None, numseqs)
sample_key = initial_states[-1][0]
initial_states = list(jax.tree_map(lambda x: x[i], initial_states[:-1]) for i in range(numseqs))
# Get repetition penalty from the arguments
repetition_penalty = sampler_options.pop('repetition_penalty', None)
def generate_scan_fn(carry, sampler_input):
# Unpack current generate_scan_fn state
generated, generated_index, next_token, decode_state, sample_key = carry
# This is the main generation loop
def generate_loop_fn(carry):
# Unpack current generate_loop_fn state
generated, generated_index, sequence_index, next_token, decode_state = carry[0][0]
sample_key = carry[1]
# Get the pseudo-random number generator key that will
# be used by kobold_sample to randomly pick a token
sample_key, new_key = jax.random.split(sample_key)
@ -207,56 +214,54 @@ class PenalizingCausalTransformer(CausalTransformer):
# based on the logits array as a 1D array with 1 element
# (higher logit means higher probability of being
# picked, non-linearly)
next_token, sample_info = kobold_sample(
next_token = kobold_sample(
sample_key,
logits,
sampler_input,
**sampler_options,
)
# Remember what token was picked so we can repetition
# penalize it next time
# Remember what token was picked
generated = generated.at[generated_index].set(next_token[0])
generated_index += 1
# self.return_logits isn't used in this program, but
# for the sake of compatibility...
if self.return_logits:
output = (next_token, sample_info, logits[jnp.newaxis])
else:
output = (next_token, sample_info)
# Re-pack the current generate_scan_fn's state so we can
# Re-pack the current generate_loop_fn's state so we can
# get back the same variables the next time
new_carry = (generated, generated_index, next_token, new_state, new_key)
return new_carry, output
# jax.lax.scan is a function that calls generate_scan_fn
# gen_length times, each time passing a state object from
# its return value (new_carry) back into one of the
# function's arguments (carry), and of course gathering the
# token it generates each time into the "outputs" array;
# we have to use jax.lax.scan instead of a normal loop
# because of JAX's JIT-compilation shenanigans
final_state, outputs = jax.lax.scan(
generate_scan_fn,
initial_state,
xs=aux,
length=gen_length,
carry[0][0] = (generated, generated_index, sequence_index, next_token, new_state)
carry[0].append(carry[0].pop(0))
return carry[0], new_key
final_state = jax.lax.while_loop(
lambda carry: carry[0][0][1] - config["seq"] < gen_length,
generate_loop_fn,
(initial_states, sample_key),
)
return final_state, outputs
return final_state
generate_fn = hk.transform(generate_sample).apply
return generate_fn(state["params"], key, ctx, ctx_length, aux)
self.generate_xmap = jax.experimental.maps.xmap(fun=generate, in_axes=(["shard", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["shard", ...]), out_axes=["batch", ...], axis_resources={'shard': 'mp', 'batch': 'dp'})
def generate(self, ctx, ctx_length, gen_length, sampler_options, return_logits=False, soft_embeddings=None):
return generate_fn(state["params"], key, ctx, ctx_length)
self.generate_xmap = jax.experimental.maps.xmap(
fun=generate,
in_axes=(
["shard", ...],
["batch", ...],
["batch", ...],
["batch", ...],
["batch", ...],
["batch", ...],
["batch", ...],
["shard", ...],
),
out_axes=["shard", "batch", ...],
axis_resources={'shard': 'mp', 'batch': 'dp'},
)
def generate(self, ctx, ctx_length, gen_length, numseqs, sampler_options, return_logits=False, soft_embeddings=None):
assert not return_logits
key = hk.PRNGSequence(random.randint(0, 2 ** 60))
batch_size = ctx.shape[0]
aux = jnp.zeros((batch_size, gen_length), dtype=jnp.uint32)
self.gen_length = gen_length
self.batch_size = batch_size
self.return_logits = return_logits
return self.generate_xmap(
self.state,
jnp.array(key.take(batch_size)),
ctx,
np.array(ctx_length, dtype=np.uint32),
aux,
np.array(gen_length, dtype=np.uint32),
np.empty((batch_size, numseqs), dtype=np.uint8),
sampler_options,
soft_embeddings,
)
@ -275,7 +280,7 @@ def infer(
soft_tokens: Optional[np.array] = None,
) -> List[str]:
maps.thread_resources.env = thread_resources_env
total_batch = numseqs
total_batch = 1
tokens = np.uint32(tokenizer.encode(context, max_length=params["seq"] - (soft_tokens.shape[0] if soft_tokens is not None else 0), truncation=True))
if(soft_tokens is not None):
tokens = np.uint32(np.concatenate((soft_tokens, tokens)))
@ -283,7 +288,6 @@ def infer(
pad_amount = seq - provided_ctx
padded_tokens = np.pad(tokens, ((pad_amount, 0),), constant_values=pad_token_id)
batched_tokens = np.array([padded_tokens] * total_batch)
length = np.ones(total_batch, dtype=np.uint32) * provided_ctx
samples = []
batched_generator_params = {
"temp": temp * np.ones(total_batch),
@ -294,14 +298,14 @@ def infer(
}
output = network.generate(
batched_tokens,
length,
gen_len,
np.ones(total_batch, dtype=np.uint32) * provided_ctx,
np.ones(total_batch, dtype=np.uint32) * gen_len,
numseqs,
batched_generator_params,
soft_embeddings=soft_embeddings,
)
decoded_tokens = output[1][0]
for o in decoded_tokens[:, :, 0]:
samples.append(tokenizer.decode(o))
)[0]
for o in output:
samples.append(tokenizer.decode(o[0][0, 0, params["seq"] : params["seq"] + gen_len]))
return samples
@ -326,6 +330,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
if param not in params:
params[param] = default_params[param]
# Disable JAX warnings about these two functions having been renamed
jax.host_count = jax.process_count
jax.host_id = jax.process_index
print("Connecting to your Colab instance's TPU", flush=True)
spinner = multiprocessing.Process(target=show_spinner, args=())
spinner.start()
@ -342,7 +350,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
params["optimizer"] = optax.scale(0)
mesh_shape = (1, cores_per_replica)
devices = np.array(jax.devices()[:cores_per_replica]).reshape(mesh_shape)
thread_resources_env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')))
thread_resources_env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')), ())
maps.thread_resources.env = thread_resources_env
tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')