# syntax=docker/dockerfile:1.7
# DeepSeek-V4-Flash-DSpark — instanttensor on top of
# aidendle94/sparkrun-vllm-ds4-gb10:production-3.75
#
# production-3.75 already ships the patched NCCL 2.30.4 ABI (its base layers
# are byte-identical to production-3.2, which is labeled "patched NCCL
# 2.30.4"), so no NCCL re-alignment is needed. This overlay only adds
# instanttensor for faster model loading (load-format instanttensor).
#
# Build: cd docker-images/deepseek-v4-flash-dspark && docker build -t sparkrun-vllm-ds4-gb10:fixed .
# Tag:   docker tag sparkrun-vllm-ds4-gb10:fixed aidendle94/sparkrun-vllm-ds4-gb10:production-3.75

FROM aidendle94/sparkrun-vllm-ds4-gb10:production-3.75

RUN pip install --no-cache-dir --no-deps --force-reinstall instanttensor==0.1.9

# Fix reasoning_effort handling in the vendored deepseek_v4 tokenizer:
# - high was accepted but silently injected nothing (only max triggered text)
# - low / unknown values were coerced to high (unreachable low, silent escalation)
# - the bundled deepseek_v4_encoding.py was an OLD revision (single REASONING_EFFORT_MAX
#   wording, max used the high text, read the deprecated `reasoning` key)
# deepseek_v4_encoding.py is replaced with the canonical model-card reference
# (deepseek-ai/DeepSeek-V4-Flash-0731 encoding/encoding_dsv4.py): full low/high/max
# prompt dict, correct gate for both high and max, and reasoning_content key.
# deepseek_v4.py carries the normalization fix so low is reachable and unknown
# values map to None. See deepseek_v4.py / deepseek_v4_encoding.py in patches/.
COPY patches/deepseek_v4.py \
     patches/deepseek_v4_encoding.py \
     /opt/venv/lib/python3.12/site-packages/vllm/tokenizers/