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add g++ compiler to base image and implement hardware-aware FeedForward sharding #413

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Merged
copybara-service merged 1 commit into from
May 21, 2026
Merged

add g++ compiler to base image and implement hardware-aware FeedForward sharding #413

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1 change: 1 addition & 0 deletions dependencies/requirements/base_requirements/requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
absl-py
accelerate
aqtp
av
chex
datasets
einops
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1 change: 1 addition & 0 deletions dependencies/requirements/generated_requirements/requirements.txt
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Expand Up @@ -15,6 +15,7 @@ astroid>=4.0.4
astunparse>=1.6.3
attrs>=25.4.0
auditwheel>=6.6.0
av>=17.0.1
black>=25.12.0
build>=1.4.0
certifi>=2026.1.4
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2 changes: 1 addition & 1 deletion maxdiffusion_dependencies.Dockerfile
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Expand Up @@ -17,7 +17,7 @@ ENV DEBIAN_FRONTEND=noninteractive
RUN python -m pip install --upgrade pip uv --no-warn-script-location

# Install system dependencies
RUN apt-get update && apt-get install -y apt-utils git curl gnupg procps iproute2 ethtool && rm -rf /var/lib/apt/lists/*
RUN apt-get update && apt-get install -y apt-utils git curl gnupg procps iproute2 ethtool g++ && rm -rf /var/lib/apt/lists/*

# Add the Google Cloud SDK package repository
RUN curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg | gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg && \
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22 changes: 19 additions & 3 deletions src/maxdiffusion/models/attention_flax.py
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Expand Up @@ -1134,6 +1134,22 @@ def __init__(
):
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim

tpu_type = get_tpu_type()
is_ironwood = tpu_type == TpuType.TPU_7X

# Hardware-aware sharding specs: Ironwood (v7x) keeps the embedding dimension (embed)
# replicated (None) to minimize cross-device communication, while other hardware (default)
# shards it to prevent OOM issues.
if is_ironwood:
net0_kernel_spec = (None, "mlp")
net2_kernel_spec = ("mlp", None)
net2_bias_spec = (None,)
else:
net0_kernel_spec = ("embed", "mlp")
net2_kernel_spec = ("mlp", "embed")
net2_bias_spec = ("embed",)

self.net_0 = nnx.Linear(
dim,
inner_dim,
Expand All @@ -1142,7 +1158,7 @@ def __init__(
dtype=dtype,
param_dtype=weights_dtype,
precision=precision,
kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), (None, "mlp")),
kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), net0_kernel_spec),
bias_init=nnx.with_partitioning(nnx.initializers.zeros, ("mlp",)),
)
self.act = get_activation(activation_fn)
Expand All @@ -1154,8 +1170,8 @@ def __init__(
dtype=dtype,
param_dtype=weights_dtype,
precision=precision,
kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), ("mlp", None)),
bias_init=nnx.with_partitioning(nnx.initializers.zeros, (None,)),
kernel_init=nnx.with_partitioning(nnx.initializers.lecun_normal(), net2_kernel_spec),
bias_init=nnx.with_partitioning(nnx.initializers.zeros, net2_bias_spec),
)

def __call__(self, hidden_states: Array) -> Array:
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