xmaps for moving things onto TPU

aiserver.py

@@ -993,7 +993,7 @@ else:
                 -1,
                 tpu_mtj_backend.params["d_model"],
             )
-            vars.sp = tensor
+            vars.sp = tpu_mtj_backend.shard_xmap(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,
@@ -4005,7 +4005,7 @@ def spRequest(filename):
             -1,
             tpu_mtj_backend.params["d_model"],
         )
-        vars.sp = np.float32(tensor)
+        vars.sp = tpu_mtj_backend.shard_xmap(np.float32(tensor))
     else:
         vars.sp = torch.from_numpy(tensor)
 

tpu_mtj_backend.py

@@ -1,5 +1,5 @@
 import multiprocessing
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, TypeVar
 import progressbar
 import time
 import os
@@ -28,6 +28,31 @@ def show_spinner():
         time.sleep(0.1)
         i += 1
 
+
+__F = TypeVar("__F", bound=Callable)
+__T = TypeVar("__T")
+
+def __move_xmap(f: __F, out_axis: str) -> __F:
+    return maps.xmap(
+        f,
+        in_axes=(["shard", ...], ["batch", ...]),
+        out_axes=[out_axis, ...],
+        axis_resources={'shard': 'mp', 'batch': 'dp'},
+    )
+
+def __shard_xmap(batch_dim=1):
+    xmap = __move_xmap(lambda s, b: s, "shard")
+    def inner(x: __T) -> __T:
+        return xmap(x, np.empty(batch_dim))
+    return inner
+
+def __batch_xmap(shard_dim=1):
+    xmap = __move_xmap(lambda s, b: b, "batch")
+    def inner(x: __T) -> __T:
+        return xmap(np.empty(shard_dim), x)
+    return inner
+
+
 def apply_repetition_penalty(logits, tokens, repetition_penalty):
     '''
     This gets called by generate_loop_fn to apply repetition penalty
@@ -255,16 +280,17 @@ class PenalizingCausalTransformer(CausalTransformer):
         key = hk.PRNGSequence(random.randint(0, 2 ** 60))
         batch_size = ctx.shape[0]
         self.batch_size = batch_size
-        return self.generate_xmap(
-            self.state,
-            jnp.array(key.take(batch_size)),
-            ctx,
-            np.array(ctx_length, dtype=np.uint32),
-            np.array(gen_length, dtype=np.uint32),
+        xargs = (
+            shard_xmap(self.state),
+            batch_xmap(jnp.array(key.take(batch_size))),
+            batch_xmap(ctx),
+            batch_xmap(np.array(ctx_length, dtype=np.uint32)),
+            batch_xmap(np.array(gen_length, dtype=np.uint32)),
             np.empty((batch_size, numseqs), dtype=np.uint8),
-            sampler_options,
-            soft_embeddings,
+            batch_xmap(sampler_options),
+            shard_xmap(soft_embeddings),
         )
+        return self.generate_xmap(*xargs)
 
 
 def infer(
@@ -354,6 +380,10 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
     maps.thread_resources.env = thread_resources_env
     tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
 
+    global shard_xmap, batch_xmap
+    shard_xmap = __shard_xmap()
+    batch_xmap = __batch_xmap(shard_dim=cores_per_replica)
+
     if not path.endswith("/"):
         path += "/"