Add TPU support for dynamic WI scan and generation modifiers

tpu_mtj_backend.py

@@ -1,5 +1,5 @@
 import multiprocessing
-from typing import Any, Callable, Dict, List, Optional, TypeVar
+from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar
 import progressbar
 import time
 import os
@@ -20,6 +20,10 @@ from mesh_transformer.transformer_shard import CausalTransformer, CausalTransfor
 params: Dict[str, Any] = {}
 
 
+def warper_callback(generated, logits, excluded_world_info, n_generated) -> Tuple[bool, bool]:
+    raise NotImplementedError("`tpu_mtj_backend.warper_callback()` needs to be defined")
+
+
 def show_spinner():
     bar = progressbar.ProgressBar(max_value=progressbar.UnknownLength, widgets=[progressbar.Timer(), '  ', progressbar.BouncingBar(left='[', right=']', marker='█')])
     i = 0
@@ -235,13 +239,13 @@ class PenalizingCausalTransformer(CausalTransformer):
             def generate_initial_inner(context, ctx_length):
                 # Give the initial context to the transformer
                 transformer = CausalTransformerShard(config)
-                def generate_initial_scan_fn(sequence_index, _):
-                    _, initial_state = transformer.generate_initial(context, ctx_length, soft_embeddings=soft_embeddings)
+                def generate_initial_scan_fn(sequence_index, c):
+                    _, initial_state = transformer.generate_initial(c, ctx_length, soft_embeddings=soft_embeddings)
                     generated_index = config["seq"]
                     # Add that information to generate_loop_fn's starting state
                     initial_state = (jnp.empty(config["n_vocab"], dtype=jnp.float32), generated_index, sequence_index) + initial_state
                     return sequence_index+1, initial_state
-                _, initial_states = jax.lax.scan(generate_initial_scan_fn, 0, None, numseqs)
+                _, initial_states = jax.lax.scan(generate_initial_scan_fn, 0, context, numseqs)
                 sample_key = initial_states[-1][0]
                 initial_states = list(list(jax.tree_map(lambda x: x[i], initial_states[:-1])) for i in range(numseqs))
                 return initial_states, sample_key
@@ -307,7 +311,8 @@ class PenalizingCausalTransformer(CausalTransformer):
             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):
+    def generate(self, ctx, ctx_length, gen_length, numseqs, sampler_options, return_logits=False, soft_embeddings=None, excluded_world_info=None, use_callback=True):
+        assert excluded_world_info is not None
         assert not return_logits
         assert gen_length.ndim == 1
         assert soft_embeddings is not None
@@ -318,24 +323,34 @@ class PenalizingCausalTransformer(CausalTransformer):
         numseqs_aux = batch_xmap(_numseqs_aux)
         sample_data = [
             [
-                np.pad(ctx[0], (0, params["seq"]), constant_values=pad_token_id),
+                np.pad(ctx[0][i], (0, params["seq"]), constant_values=pad_token_id),
                 params["seq"],
                 None,
                 np.empty((), dtype=np.uint32),
             ]
-            for _ in range(numseqs)
+            for i in range(numseqs)
         ]
         repetition_penalty = sampler_options.pop("repetition_penalty", 1.0)
+        n_generated = 0
+        regeneration_required = False
+        halt = False
         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 = np.asarray(sample_key[0, 0])
-        for _ in range(gen_length[0].item()):
+        while True:
             generate_data, = self.generate_once_xmap(generate_data, self.state, numseqs_aux, soft_embeddings)
             for i in range(numseqs):
-                sample_data[i][2] = np.array(generate_data[0][i][0, 0], copy=True)
+                sample_data[i][2] = np.array(generate_data[i][0][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
+            n_generated += 1
+            if use_callback:
+                excluded_world_info, regeneration_required, halt = warper_callback(np.uint32(tuple(d[0] for d in sample_data)), np.float32(tuple(d[2] for d in sample_data)), excluded_world_info, n_generated)
+                if regeneration_required or halt:
+                    break
+            else:
+                break
+        return sample_data, n_generated, regeneration_required, halt
 
 
 def infer(
@@ -349,15 +364,18 @@ def infer(
     gen_len=80,
     soft_embeddings: Optional[np.array] = None,
     soft_tokens: Optional[np.array] = None,
-) -> List[str]:
+    excluded_world_info = None,
+    use_callback=True,
+) -> Tuple[List[np.array], int, bool, bool]:
+    assert excluded_world_info is not None
     maps.thread_resources.env = thread_resources_env
     total_batch = 1
     tokens = context
     if(soft_tokens is not None):
-        tokens = np.uint32(np.concatenate((soft_tokens, tokens)))
-    provided_ctx = tokens.shape[0]
+        tokens = np.uint32(np.concatenate((np.tile(soft_tokens, (tokens.shape[0], 1)), tokens), axis=-1))
+    provided_ctx = tokens.shape[-1]
     pad_amount = seq - provided_ctx
-    padded_tokens = np.pad(tokens, ((pad_amount, 0),), constant_values=pad_token_id)
+    padded_tokens = np.pad(tokens, ((0, 0), (pad_amount, 0)), constant_values=pad_token_id)
     batched_tokens = np.array([padded_tokens] * total_batch)
     samples = []
     generator_params = {
@@ -374,10 +392,12 @@ def infer(
         numseqs,
         generator_params,
         soft_embeddings=soft_embeddings,
-    )[0]
-    for out in output:
+        excluded_world_info=excluded_world_info,
+        use_callback=use_callback,
+    )
+    for out in output[0]:
         samples.append(out[0][params["seq"] : params["seq"] + gen_len])
-    return samples
+    return (samples,) + output[1:]
 
 
 def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs) -> None:
@@ -405,32 +425,25 @@ 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()
-        colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0]
-        url = f'http://{colab_tpu_addr}:8475/requestversion/{driver_version}'
-        requests.post(url)
-        spinner.terminate()
-        print()
-        config.FLAGS.jax_xla_backend = "tpu_driver"
-        config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR']
+    print("Connecting to your Colab instance's TPU", flush=True)
+    spinner = multiprocessing.Process(target=show_spinner, args=())
+    spinner.start()
+    colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0]
+    url = f'http://{colab_tpu_addr}:8475/requestversion/{driver_version}'
+    requests.post(url)
+    spinner.terminate()
+    print()
+    config.FLAGS.jax_xla_backend = "tpu_driver"
+    config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR']
 
-        cores_per_replica = params["cores_per_replica"]
-        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
+    cores_per_replica = params["cores_per_replica"]
+    seq = params["seq"]
+    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')), ())
+    maps.thread_resources.env = thread_resources_env
+    tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
 
     global shard_xmap, batch_xmap
     shard_xmap = __shard_xmap()