Merge pull request #35 from VE-FORBRYDERNE/sp
Softprompt support for the TPU backend
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henk717
GitHub
commit
d877190258
32
aiserver.py
32
aiserver.py
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@ -108,6 +108,7 @@ class vars:
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loadselect = "" # Temporary storage for story filename to load
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spselect = "" # Temporary storage for soft prompt filename to load
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sp = None # Current soft prompt tensor (as a NumPy array)
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sp_length = 0 # Length of current soft prompt in tokens, or 0 if not using a soft prompt
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svowname = "" # Filename that was flagged for overwrite confirm
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saveow = False # Whether or not overwrite confirm has been displayed
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genseqs = [] # Temporary storage for generated sequences
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@ -700,6 +701,8 @@ else:
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assert vars.model == "TPUMeshTransformerGPTJ" and vars.custmodpth and os.path.isdir(vars.custmodpth)
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import tpu_mtj_backend
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tpu_mtj_backend.load_model(vars.custmodpth)
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vars.allowsp = True
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vars.modeldim = int(tpu_mtj_backend.params["d_model"])
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tokenizer = tpu_mtj_backend.tokenizer
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# Set up Flask routes
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@ -1684,10 +1687,17 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
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# Submit input text to generator
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try:
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if(vars.sp is not None):
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raise ValueError("Softprompts are not supported by the TPU backend yet")
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if(vars.dynamicscan):
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raise ValueError("Dynamic world info scanning is not supported by the TPU backend yet")
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soft_tokens = None
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if(vars.sp is not None):
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soft_tokens = np.arange(
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tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"],
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tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"] + vars.sp_length,
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dtype=np.uint32
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)
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genout = tpu_mtj_backend.infer(
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txt,
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gen_len = maximum-minimum+1,
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@ -1697,6 +1707,8 @@ def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
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tfs=vars.tfs,
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numseqs=vars.numseqs,
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repetition_penalty=vars.rep_pen,
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soft_embeddings=vars.sp,
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soft_tokens=soft_tokens,
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)
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except Exception as e:
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@ -2525,6 +2537,7 @@ def loadRequest(loadpath, filename=None):
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def spRequest(filename):
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if(len(filename) == 0):
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vars.sp = None
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vars.sp_length = 0
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return
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global np
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@ -2548,7 +2561,20 @@ def spRequest(filename):
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tensor = np.float32(tensor)
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assert not np.isinf(tensor).any() and not np.isnan(tensor).any()
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vars.sp = torch.from_numpy(tensor)
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vars.sp_length = tensor.shape[0]
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if(vars.model in ("TPUMeshTransformerGPTJ",)):
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rows = tensor.shape[0]
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padding_amount = -(rows % -tpu_mtj_backend.params["cores_per_replica"])
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tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
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tensor = tensor.reshape(
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tpu_mtj_backend.params["cores_per_replica"],
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-1,
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tpu_mtj_backend.params["d_model"],
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)
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vars.sp = np.float32(tensor)
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else:
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vars.sp = torch.from_numpy(tensor)
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#==================================================================#
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# Import an AIDungon game exported with Mimi's tool
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@ -1,5 +1,5 @@
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import multiprocessing
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from typing import Any, Dict, List
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from typing import Any, Dict, List, Optional
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import progressbar
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import time
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import os
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@ -155,14 +155,14 @@ class PenalizingCausalTransformer(CausalTransformer):
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def __init__(self, config):
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# Initialize
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super().__init__(config)
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def generate(state, key, ctx, ctx_length, aux, sampler_options):
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def generate(state, key, ctx, ctx_length, aux, sampler_options, soft_embeddings=None):
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gen_length = self.gen_length
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# These are the tokens that we don't want the AI to ever write
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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])
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def generate_sample(context, ctx_length, aux):
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# Give the initial context to the transformer
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transformer = CausalTransformerShard(config)
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_, initial_state = transformer.generate_initial(context, ctx_length)
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_, initial_state = transformer.generate_initial(context, ctx_length, soft_embeddings=soft_embeddings)
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# The "generated" array will contain the tokens from the
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# context as well as the tokens picked by the sampler at
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# each stage, padded with a bunch of 50256s, so we know
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@ -185,7 +185,7 @@ class PenalizingCausalTransformer(CausalTransformer):
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# how strongly it thinks each of the 50257 tokens in its
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# vocabulary should be appended to the context, followed
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# by 143 apparently useless columns ???)
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logits, new_state = transformer.generate_once(next_token, decode_state)
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logits, new_state = transformer.generate_once(next_token, decode_state, soft_embeddings=soft_embeddings)
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# Verify that logits does indeed have that many rows and
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# columns (if you get an error here, pray for mercy)
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assert logits.shape == (1, config["n_vocab"])
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@ -243,8 +243,8 @@ class PenalizingCausalTransformer(CausalTransformer):
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return final_state, outputs
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generate_fn = hk.transform(generate_sample).apply
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return generate_fn(state["params"], key, ctx, ctx_length, aux)
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self.generate_xmap = jax.experimental.maps.xmap(fun=generate, in_axes=(["shard", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["batch", ...], ["batch", ...]), out_axes=["batch", ...], axis_resources={'shard': 'mp', 'batch': 'dp'})
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def generate(self, ctx, ctx_length, gen_length, sampler_options, return_logits=False):
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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'})
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def generate(self, ctx, ctx_length, gen_length, sampler_options, return_logits=False, soft_embeddings=None):
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key = hk.PRNGSequence(random.randint(0, 2 ** 60))
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batch_size = ctx.shape[0]
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aux = jnp.zeros((batch_size, gen_length), dtype=jnp.uint32)
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@ -257,19 +257,33 @@ class PenalizingCausalTransformer(CausalTransformer):
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ctx,
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np.array(ctx_length, dtype=np.uint32),
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aux,
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sampler_options
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sampler_options,
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soft_embeddings,
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)
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def infer(context, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, repetition_penalty=1.0, numseqs=1, gen_len=80) -> List[str]:
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def infer(
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context: str,
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top_p=0.9,
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temp=0.5,
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top_k=0,
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tfs=1.0,
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repetition_penalty=1.0,
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numseqs=1,
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gen_len=80,
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soft_embeddings: Optional[np.array] = None,
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soft_tokens: Optional[np.array] = None,
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) -> List[str]:
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maps.thread_resources.env = thread_resources_env
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total_batch = numseqs
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tokens = tokenizer.encode(context, max_length=params["seq"], truncation=True)
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provided_ctx = len(tokens)
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tokens = np.uint32(tokenizer.encode(context, max_length=params["seq"] - soft_tokens.shape[0] if soft_tokens is not None else 0, truncation=True))
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if(soft_tokens is not None):
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tokens = np.uint32(np.concatenate((soft_tokens, tokens)))
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provided_ctx = tokens.shape[0]
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pad_amount = seq - provided_ctx
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padded_tokens = np.pad(np.asarray(tokens, dtype=np.uint32), ((pad_amount, 0),), constant_values=pad_token_id)
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padded_tokens = np.pad(tokens, ((pad_amount, 0),), constant_values=pad_token_id)
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batched_tokens = np.array([padded_tokens] * total_batch)
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length = np.ones(total_batch, dtype=np.uint32) * len(tokens)
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length = np.ones(total_batch, dtype=np.uint32) * provided_ctx
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samples = []
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batched_generator_params = {
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"temp": temp * np.ones(total_batch),
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@ -278,7 +292,13 @@ def infer(context, top_p=0.9, temp=0.5, top_k=0, tfs=1.0, repetition_penalty=1.0
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"repetition_penalty": repetition_penalty * np.ones(total_batch),
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"top_k": np.full(total_batch, top_k, dtype=np.uint32)
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}
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output = network.generate(batched_tokens, length, gen_len, batched_generator_params)
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output = network.generate(
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batched_tokens,
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length,
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gen_len,
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batched_generator_params,
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soft_embeddings=soft_embeddings,
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)
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decoded_tokens = output[1][0]
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for o in decoded_tokens[:, :, 0]:
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samples.append(tokenizer.decode(o))
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