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KoboldAI-Client
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Convert the `jit`ted function into ordinary NumPy operations

This commit is contained in:
Gnome Ann 2022-01-14 15:05:21 -05:00
parent 57a6886007
commit 0bef92419b
1 changed files with 77 additions and 69 deletions
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110
tpu_mtj_backend.py
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@ -63,19 +63,20 @@ def apply_repetition_penalty(logits, tokens, repetition_penalty):
# logits array; e.g.
# if logits is [77, 5, 3, 98] and tokens is [0, 1, 2, 3, 2, 3, 1],
# then penalty_logits will be [77, 5, 3, 98, 3, 98, 5]
penalty_logits = jnp.take(logits, tokens)
penalty_logits = np.take(logits, tokens)
# Divide positive values by repetition_penalty and multiply negative
# values by repetition_penalty (the academic publication that described
# this technique actually just only divided, but that would cause tokens
# with negative logits to become more likely, which is obviously wrong)
penalty_logits = jnp.where(
penalty_logits = np.where(
penalty_logits > 0,
penalty_logits/repetition_penalty,
penalty_logits*repetition_penalty,
)
# Finally, put those penalized logit values back into their original
# positions in the logits array
return logits.at[tokens].set(penalty_logits)
logits[tokens] = penalty_logits
return logits
def kobold_sample(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
'''
@ -91,15 +92,16 @@ def kobold_sample(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
# in the sorted logits array we want to remove and False for ones
# we want to keep, in this case the first top_k elements will be
# False and the rest will be True
sorted_indices_to_remove = jnp.arange(len(logits)) >= top_k
sorted_indices_to_remove = np.arange(len(logits)) >= top_k
# Unsort the logits array back to its original configuration and
# remove tokens we need to remove
_, indices_to_remove = jax.lax.sort_key_val(
jnp.argsort(-logits),
np.argsort(-logits),
sorted_indices_to_remove,
)
return jnp.where(indices_to_remove, -jnp.inf, logits)
logits = jax.lax.cond(top_k > 0, top_k_filter, lambda x: x, logits)
return np.where(indices_to_remove, -np.inf, logits)
if top_k > 0:
logits = top_k_filter(logits)
# Top-p (after sorting the remaining tokens again in descending order of
# logit, remove the ones that have cumulative softmax probability
# greater than p)
@ -108,75 +110,75 @@ def kobold_sample(key, logits, top_p=0.9, temp=0.5, top_k=0, tfs=1.0):
# with e (Euler's number) to the power of that element, and divide
# each element of the new array by the sum of the elements in the
# new array
sorted_logits = -jnp.sort(-logits)
probabilities = jax.nn.softmax(sorted_logits)
sorted_logits = -np.sort(-logits)
probabilities = np.array(jax.nn.softmax(sorted_logits), copy=True)
# Calculate cumulative_probabilities as the prefix-sum array of
# probabilities
cumulative_probabilities = jnp.cumsum(probabilities, axis=-1)
cumulative_probabilities = np.cumsum(probabilities, axis=-1)
# We want to remove tokens with cumulative probability higher
# than top_p
sorted_indices_to_remove = cumulative_probabilities > top_p
# Don't ever remove the token with the highest logit, even if
# the probability is higher than top_p
sorted_indices_to_remove = sorted_indices_to_remove.at[0].set(False)
sorted_indices_to_remove[0] = False
# Unsort and remove
_, indices_to_remove = jax.lax.sort_key_val(
jnp.argsort(-logits),
np.argsort(-logits),
sorted_indices_to_remove,
)
return jnp.where(indices_to_remove, -jnp.inf, logits)
logits = jax.lax.cond(top_p < 1.0, top_p_filter, lambda x: x, logits)
return np.where(indices_to_remove, -np.inf, logits)
if top_p < 1.0:
logits = top_p_filter(logits)
# Tail free sampling (basically top-p a second time on remaining tokens
# except it's the "cumulative normalized absolute second finite
# differences of the softmax probabilities" instead of just the
# cumulative softmax probabilities)
def tail_free_filter(logits):
# Sort in descending order
sorted_logits = -jnp.sort(-logits)
sorted_logits = -np.sort(-logits)
# Softmax again
probabilities = jax.nn.softmax(sorted_logits)
probabilities = np.array(jax.nn.softmax(sorted_logits), copy=True)
# Calculate the second finite differences of that array (i.e.
# calculate the difference array and then calculate the difference
# array of the difference array)
d2 = jnp.diff(jnp.diff(probabilities))
d2 = np.diff(np.diff(probabilities))
# Get the absolute values of all those second finite differences
d2 = jnp.abs(d2)
d2 = np.abs(d2)
# Normalize (all elements in the array are divided by the sum of the
# array's elements)
d2 = d2 / d2.sum(axis=-1, keepdims=True)
# Get the prefix-sum array
cumulative_d2 = jnp.cumsum(d2, axis=-1)
cumulative_d2 = np.cumsum(d2, axis=-1)
# We will remove the tokens with a cumulative normalized absolute
# second finite difference larger than the TFS value
sorted_indices_to_remove = cumulative_d2 > tfs
# Don't remove the token with the highest logit
sorted_indices_to_remove = sorted_indices_to_remove.at[0].set(False)
sorted_indices_to_remove[0] = False
# Since the d2 array has two fewer elements than the logits array,
# we'll add two extra Trues to the end
sorted_indices_to_remove = jnp.pad(
sorted_indices_to_remove = np.pad(
sorted_indices_to_remove,
(0, 2),
constant_values=True,
)
# Unsort and remove
_, indices_to_remove = jax.lax.sort_key_val(
jnp.argsort(-logits),
np.argsort(-logits),
sorted_indices_to_remove,
)
return jnp.where(indices_to_remove, -jnp.inf, logits)
logits = jax.lax.cond(tfs < 1.0, tail_free_filter, lambda x: x, logits)
return np.where(indices_to_remove, -np.inf, logits)
if tfs < 1.0:
logits = tail_free_filter(logits)
# Temperature (just divide the logits by the temperature)
def temp_filter(logits):
return logits / temp
logits = jax.lax.cond(True, temp_filter, lambda x: x, logits)
logits /= temp
# 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)
return jax.random.categorical(key, logits, -1).astype(np.uint32)
pad_token_id = 50256
def sample_jit(data, key, numseqs_aux, badwords, repetition_penalty, sampler_options):
def sample_func(data, key, numseqs_aux, badwords, repetition_penalty, sampler_options):
numseqs = numseqs_aux.shape[0]
gi = data[0][1]
def sample_loop_fn(carry):
@ -195,7 +197,7 @@ def sample_jit(data, key, numseqs_aux, badwords, repetition_penalty, sampler_opt
# Remove any tokens in the badwords list by setting
# their logits to negative infinity which effectively
# makes their probabilities of being chosen zero
logits = logits.at[badwords].set(-jnp.inf)
logits[badwords] = -np.inf
# Use the sampler (kobold_sample) to pick one token
# based on the logits array as a 0D uint32 array
# (higher logit means higher probability of being
@ -206,18 +208,22 @@ def sample_jit(data, key, numseqs_aux, badwords, repetition_penalty, sampler_opt
**sampler_options,
)
# Remember what token was picked
generated = generated.at[generated_index].set(next_token)
generated[generated_index] = next_token
generated_index += 1
# Re-pack the current sample_loop_fn's state so we can
# get back the same variables the next time
carry[0][0] = [generated, generated_index, logits, next_token]
carry[0].append(carry[0].pop(0))
return carry[0], new_key
return jax.lax.while_loop(
lambda carry: carry[0][0][1] < gi,
sample_loop_fn,
(data, key),
)
# return jax.lax.while_loop(
# lambda carry: carry[0][0][1] == gi,
# sample_loop_fn,
# (data, key),
# )
carry = (data, key)
while carry[0][0][1] == gi:
carry = sample_loop_fn(carry)
return carry
class PenalizingCausalTransformer(CausalTransformer):
def __init__(self, config):
@ -237,7 +243,7 @@ class PenalizingCausalTransformer(CausalTransformer):
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))
initial_states = list(list(jax.tree_map(lambda x: x[i], initial_states[:-1])) for i in range(numseqs))
return initial_states, sample_key
return generate_initial_inner.apply(state["params"], key, ctx, ctx_length)
self.generate_initial_xmap = jax.experimental.maps.xmap(
@ -281,7 +287,7 @@ class PenalizingCausalTransformer(CausalTransformer):
# Re-pack the current generate_loop_fn's state so we can
# get back the same variables the next time
generated_index += 1
carry[0][0] = (logits, generated_index, sequence_index, next_token, new_state)
carry[0][0] = [logits, generated_index, sequence_index, next_token, new_state]
carry[0].append(carry[0].pop(0))
return carry[0],
return jax.lax.while_loop(
@ -312,21 +318,23 @@ class PenalizingCausalTransformer(CausalTransformer):
numseqs_aux = batch_xmap(_numseqs_aux)
sample_data = [
[
jnp.pad(ctx, (0, params["seq"]), constant_values=pad_token_id),
np.pad(ctx[0], (0, params["seq"]), constant_values=pad_token_id),
params["seq"],
None,
jnp.empty((), dtype=jnp.uint32),
np.empty((), dtype=np.uint32),
]
for _ in range(numseqs)
]
repetition_penalty = sampler_options.pop("repetition_penalty", 1.0)
generate_data, sample_key = self.generate_initial_xmap(self.state, jnp.array(key.take(batch_size)), ctx, ctx_length, numseqs_aux, soft_embeddings)
sample_key = jax.device_put(sample_key[0, 0], cpu)
sample_key = np.asarray(sample_key[0, 0])
for _ in range(gen_length[0].item()):
generate_data, = self.generate_once_xmap(generate_data, self.state, numseqs_aux, soft_embeddings)
for i in range(numseqs):
sample_data[i][2] = jax.device_put(generate_data[0][i][0, 0], cpu)
sample_data, sample_key = sample_jit(sample_data, sample_key, _numseqs_aux, badwords, repetition_penalty, sampler_options)
sample_data[i][2] = np.array(generate_data[0][i][0, 0], copy=True)
sample_data, sample_key = sample_func(sample_data, sample_key, _numseqs_aux, badwords, repetition_penalty, sampler_options)
for i in range(numseqs):
generate_data[i][3] = np.tile(sample_data[i][0][sample_data[i][1]-1][np.newaxis, np.newaxis], (params["cores_per_replica"], 1, 1))
return sample_data, sample_key
@ -368,7 +376,7 @@ def infer(
soft_embeddings=soft_embeddings,
)[0]
for out in output:
samples.append(out[0][0, 0, params["seq"] : params["seq"] + gen_len])
samples.append(out[0][params["seq"] : params["seq"] + gen_len])
return samples
@ -397,6 +405,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
jax.host_count = jax.process_count
jax.host_id = jax.process_index
while True:
print("Connecting to your Colab instance's TPU", flush=True)
spinner = multiprocessing.Process(target=show_spinner, args=())
spinner.start()
@ -412,22 +421,21 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
seq = params["seq"]
params["optimizer"] = optax.scale(0)
mesh_shape = (1, cores_per_replica)
try:
devices = np.array(jax.devices()[:cores_per_replica]).reshape(mesh_shape)
except RuntimeError as e:
if "DEADLINE_EXCEEDED" not in str(e):
raise e
continue
thread_resources_env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')), ())
maps.thread_resources.env = thread_resources_env
tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
break
global shard_xmap, batch_xmap
shard_xmap = __shard_xmap()
batch_xmap = __batch_xmap(shard_dim=cores_per_replica)
global cpu, sample_jit
cpu = jax.devices("cpu")[0]
sample_jit = jax.jit(
sample_jit,
device=cpu,
)
global badwords
# These are the tokens that we don't want the AI to ever write
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])