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[jax-inference-offloading] consolidate definitions for default tensor dtype #1816

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[jax-inference-offloading] consolidate definitions for default tensor dtype #1816

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3ad4a53
[jax-inference-offloading] Move default dtype to proto
yhtang Dec 5, 2025
83d79db
default dtype
yhtang Dec 5, 2025
0093423
Fix proto
yhtang Dec 5, 2025
5b8ea2d
Merge branch 'main' into yhtang/jio-default-dtype
yhtang Dec 5, 2025
301cd23
remove default dtype from proto
yhtang Dec 10, 2025
740c4ff
Merge branch 'main' into yhtang/jio-default-dtype
Steboss Jan 20, 2026
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2 changes: 1 addition & 1 deletion jax-inference-offloading/jax_inference_offloading/api/param_mapping.proto
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Original file line number Diff line number Diff line change
Expand Up @@ -82,4 +82,4 @@ message ParamMapping {

message TpModelMappingSpecs {
repeated ParamMapping mappings = 1;
}
}
4 changes: 3 additions & 1 deletion jax-inference-offloading/jax_inference_offloading/models/__init__.py
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Expand Up @@ -32,11 +32,12 @@ def make_transform(slice=[], transpose=[], reshape=[], replication_axis=None, re


def make_mapping(
jax_name, vllm_name, vllm_shape, *, transform=None, jax_prefix="model", vllm_prefix="model"
jax_name, vllm_name, vllm_shape, *, transform=None, jax_prefix="model", vllm_prefix="model", dtype="bfloat16"
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@jreiffers jreiffers Dec 11, 2025

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s/dtype/vllm_dtype/?

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@yhtang yhtang Dec 11, 2025 •
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At the moment we don’t support any dtype conversion between the JAX and vLLM sides, so only vllm_param carries a dtype field, and the dtypes are expected to match between JAX and vLLM. Once we add conversion support, it may even make sense to stop specifying dtype in make_mapping altogether and instead rely on the handshake to discover the dtype at runtime.

):
result = mapping.ParamMapping()
result.vllm_param.name = f"{vllm_prefix}.{vllm_name}".lstrip(".")
result.vllm_param.shape.extend(vllm_shape)
result.vllm_param.dtype = dtype
result.jax_param.name = f"{jax_prefix}.{jax_name}".lstrip(".")
if transform is not None:
result.jax_param.transform.CopyFrom(transform)
Expand All @@ -62,3 +63,4 @@ def get_named_parameters(nnx_model, prefix="model", *filters):

nnx_state = nnx.state(nnx_model, *filters)
return flatten_state(nnx_state, prefix=prefix)

8 changes: 4 additions & 4 deletions jax-inference-offloading/jax_inference_offloading/vllm/extension.py
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Expand Up @@ -187,7 +187,7 @@ def update_weights(self, mapping_specs: TpModelMappingSpecs):

logger.debug(f'vLLM TP rank {tp_rank} receiving {param.vllm_param.name} ...')
weight = self.transport.gather(
shape, param.vllm_param.dtype or 'bfloat16',
shape, param.vllm_param.dtype,
sharding_specs.aux_dim, sharding_specs.aux_parallelism
)
logger.debug(f'vLLM TP rank {tp_rank} received {param.vllm_param.name} shape {weight.shape}')
Expand All @@ -206,7 +206,7 @@ def update_weights(self, mapping_specs: TpModelMappingSpecs):

logger.debug(f"vLLM expecting: {param.vllm_param.name} shape {shape.tolist()} raw specs {param}")

weight = self.transport.recv(shape, param.vllm_param.dtype or 'bfloat16')
weight = self.transport.recv(shape, param.vllm_param.dtype)
self._staged_weights.append((param.vllm_param.name, weight))

# TODO: make it optional
Expand Down Expand Up @@ -235,7 +235,7 @@ def update_weights_grouped(self, mapping_specs: TpModelMappingSpecs):

param_specs.append((
shape,
param.vllm_param.dtype or 'bfloat16',
param.vllm_param.dtype,
sharding_specs.aux_dim,
sharding_specs.aux_parallelism
))
Expand Down Expand Up @@ -264,7 +264,7 @@ def update_weights_grouped(self, mapping_specs: TpModelMappingSpecs):
if sharding_specs.parallelism > 0:
shape[sharding_specs.dim] //= sharding_specs.parallelism

param_specs.append((shape, param.vllm_param.dtype or 'bfloat16'))
param_specs.append((shape, param.vllm_param.dtype))
param_names.append(param.vllm_param.name)

# Receive all weights in one grouped operation
Expand Down
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