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Add a CPU check and CI (#15) #25

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Add a CPU check and CI (#15) #25

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326c5fa
Add a CPU check and CI (#15)
tengyifei Jan 9, 2025
140a845
different init
qihqi Jan 10, 2025
35d7e49
skip keys not needed
qihqi Jan 10, 2025
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42 changes: 31 additions & 11 deletions torchprime/experimental/torchax_models/run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@

import custom_mesh
import jax
from jax import numpy as jnp
import numpy as np
import splash_attn
import torch
Expand Down Expand Up @@ -142,17 +143,36 @@ def make_weight_shard(weight_meta, slice_index):


def create_sharded_weights(model, mesh, sharding_map):
res = {}
for name, weight_meta in model.state_dict().items():
sharding_spec = sharding_map.get(_process_sharding_name(name))
if sharding_spec is None:
print("Skipping weight:", name)
continue
sharding = NamedSharding(mesh, P(*sharding_spec))
res[name] = jax.make_array_from_callback(
weight_meta.shape, sharding,
functools.partial(make_weight_shard, weight_meta))
return res
name_to_sharding = {
name: NamedSharding(mesh, P(*sharding_map.get(_process_sharding_name(name))))
for name in model.state_dict().keys()
if _process_sharding_name(name) in sharding_map
}

kaiming = jax.nn.initializers.he_uniform(dtype=jnp.bfloat16)

key = jax.random.PRNGKey(0)
key = jax.device_put(key, NamedSharding(mesh, P())) # replicate

@functools.partial(
jax.jit,
out_shardings=name_to_sharding,
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Did you test this on 405B? When I did something similar locally, the jit worked for 8B but the graph used >1TiB of HBM on 405B on two slices. The problem I encountered was that each device was still trying to generate the entire weight.

)
def create_weights(rng):
res = {}
for name, weight_meta in model.state_dict().items():
if _process_sharding_name(name) not in sharding_map:
continue
rng, subkey = jax.random.split(rng)
if len(weight_meta.shape) < 2:
res[name] = jax.random.normal(subkey, weight_meta.shape,
interop.jax_view(weight_meta.dtype))
else:
res[name] = kaiming(subkey, weight_meta.shape, interop.jax_view(weight_meta.dtype))
return res

weights = create_weights(key)
return interop.torch_view(weights)


def sharded_device_put(tensor, sharding):
Expand Down
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