i have no idea

V3.2 __init__ sets self.tool_call_start_token and self.tool_call_end_token
as instance attributes with function_calls tag values, shadowing the V4
class-level attributes that use tool_calls tags. Re-assign after super().

Dockerfile

@@ -1,8 +1,9 @@
 #FROM vllm/vllm-openai:v0.19.0-cu130
-FROM vllm/vllm-openai:cu130-nightly-x86_64
+#FROM vllm/vllm-openai:deepseekv4-cu130
+FROM vllm/vllm-openai:v0.20.0-cu130
 
 # Fix the broken ass nightly build that forgot to include pandas
-RUN pip install --no-cache-dir pandas
+#RUN pip install --no-cache-dir pandas
 
 # Install LMCache for KV cache offloading / sharing across nodes
 # Build with system CUDA 13.0 for Blackwell (B200)
@@ -12,6 +13,7 @@ RUN apt-get update && apt-get install -y git \
     libcublas-dev-13-0 \
     libcurand-dev-13-0 \
     libcufft-dev-13-0 \
+    cuda-nvrtc-dev-13-0 \
     libnvjitlink-dev-13-0 && \
     git clone https://github.com/biondizzle/LMCache.git /tmp/lmcache && \
     cd /tmp/lmcache && \
@@ -19,17 +21,29 @@ RUN apt-get update && apt-get install -y git \
     CUDA_HOME=/usr/local/cuda \
     TORCH_CUDA_ARCH_LIST="10.0" \
     pip install --no-cache-dir --no-build-isolation . && \
-    rm -rf /tmp/lmcache
+    rm -rf /tmp/lmcache && export CACHE_BUSTER=2
+
+ENV VLLM_DOCKER_BUILD_CONTEXT=1
+# Needs git to be installed before this can run
+#RUN curl -fsSL https://raw.githubusercontent.com/vllm-project/vllm/v0.20.0/tools/install_deepgemm.sh | bash
+# Had to modify this
+COPY ./install_deepgemm.sh /opt/install_deepgemm.sh
+RUN chmod +x /opt/install_deepgemm.sh
+RUN /opt/install_deepgemm.sh
 
 # Copy over nemotron reasonong parser
-COPY ./super_v3_reasoning_parser.py /opt/super_v3_reasoning_parser.py
+#COPY ./super_v3_reasoning_parser.py /opt/super_v3_reasoning_parser.py
 
 # Copy over deepseek tool call parser with MTP fixes
-COPY deepseekv32_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/deepseekv32_tool_parser.py
+#COPY deepseekv32_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/deepseekv32_tool_parser.py
+#COPY deepseekv4_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/deepseekv4_tool_parser.py
 
 # Copy over minimax tool call parser with kwargs fixes
-COPY minimax_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/minimax_tool_parser.py
+#COPY minimax_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/minimax_tool_parser.py
 
 # Copy over minimax parsers with kwargs fixes
-COPY minimax_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/minimax_tool_parser.py
-COPY minimax_m2_parser.py /usr/local/lib/python3.12/dist-packages/vllm/parser/minimax_m2_parser.py
+#COPY minimax_tool_parser.py /usr/local/lib/python3.12/dist-packages/vllm/tool_parsers/minimax_tool_parser.py
+#COPY minimax_m2_parser.py /usr/local/lib/python3.12/dist-packages/vllm/parser/minimax_m2_parser.py
+
+# Fix DeepseekV4Config missing max_position_embeddings attr for RoPE standardization
+#COPY deepseek_v4.py /usr/local/lib/python3.12/dist-packages/vllm/transformers_utils/configs/deepseek_v4.py

deepseek_v4.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from transformers import PretrainedConfig
+
+
+class DeepseekV4Config(PretrainedConfig):
+    model_type = "deepseek_v4"
+
+    def __init__(
+        self,
+        vocab_size: int = 129024,
+        hidden_size: int = 7168,
+        intermediate_size: int = 18432,
+        moe_intermediate_size: int = 1408,
+        num_hidden_layers: int = 61,
+        num_attention_heads: int = 128,
+        num_key_value_heads: int = 128,
+        n_routed_experts: int = 256,
+        n_shared_experts: int = 1,
+        num_experts_per_tok: int = 8,
+        head_dim: int = 256,
+        q_lora_rank: int = 1536,
+        o_lora_rank: int = 1536,
+        qk_rope_head_dim: int = 64,
+        o_groups: int = 128,
+        rms_norm_eps: float = 1e-6,
+        rope_theta: float = 10000.0,
+        rope_scaling: dict | None = None,
+        max_position_embeddings: int = 163840,
+        hidden_act: str = "silu",
+        norm_topk_prob: bool = True,
+        compress_ratios: list | None = None,
+        compress_rope_theta: float = 10000.0,
+        num_hash_layers: int = 0,
+        hc_mult: float = 1.0,
+        hc_eps: float = 1e-6,
+        hc_sinkhorn_iters: int = 8,
+        swiglu_limit: int = 0,
+        index_topk: int = 0,
+        num_nextn_predict_layers: int = 0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.head_dim = head_dim
+        self.q_lora_rank = q_lora_rank
+        self.o_lora_rank = o_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.o_groups = o_groups
+        self.rms_norm_eps = rms_norm_eps
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_act = hidden_act
+        self.norm_topk_prob = norm_topk_prob
+        self.compress_ratios = compress_ratios or [1] * num_hidden_layers
+        self.compress_rope_theta = compress_rope_theta
+        self.num_hash_layers = num_hash_layers
+        self.hc_mult = hc_mult
+        self.hc_eps = hc_eps
+        self.hc_sinkhorn_iters = hc_sinkhorn_iters
+        self.swiglu_limit = swiglu_limit
+        self.index_topk = index_topk
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        super().__init__(**kwargs)

deepseekv4_tool_parser.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import regex as re
+
+from vllm.tool_parsers.deepseekv32_tool_parser import DeepSeekV32ToolParser
+
+
+class DeepSeekV4ToolParser(DeepSeekV32ToolParser):
+    """
+    DeepSeek V4 DSML tool parser.
+
+    V4 keeps the V3.2 DSML invoke/parameter grammar, but wraps tool calls in
+    ``<｜DSML｜tool_calls>`` instead of ``<｜DSML｜function_calls>``.
+    """
+
+    tool_call_start_token: str = "<｜DSML｜tool_calls>"
+    tool_call_end_token: str = "</｜DSML｜tool_calls>"
+
+    def __init__(self, tokenizer, tools=None):
+        super().__init__(tokenizer, tools)
+        # The V3.2 __init__ overwrites the class-level start/end tokens
+        # with instance attributes set to "function_calls". Re-override
+        # them to the V4 "tool_calls" tags so _extract_content(),
+        # _extract_invoke_regions(), etc. match the correct tags.
+        self.tool_call_start_token = "<｜DSML｜tool_calls>"
+        self.tool_call_end_token = "</｜DSML｜tool_calls>"
+        self.tool_calls_start_token = self.tool_call_start_token
+        # Recompile regexes with the V4 tool_calls tags instead of
+        # the V3.2 function_calls tags that the base class hardcodes.
+        self.tool_call_complete_regex = re.compile(
+            r"<｜DSML｜tool_calls>(.*?)</｜DSML｜tool_calls>", re.DOTALL
+        )

install_deepgemm.sh

@@ -0,0 +1,121 @@
+#!/bin/bash
+# Script to build and/or install DeepGEMM from source
+# Default: build and install immediately
+# Optional: build wheels to a directory for later installation (useful in multi-stage builds)
+set -e
+
+# Default values
+# Keep DEEPGEMM_GIT_REF in sync with cmake/external_projects/deepgemm.cmake
+DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git"
+DEEPGEMM_GIT_REF="891d57b4db1071624b5c8fa0d1e51cb317fa709f"
+WHEEL_DIR=""
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --ref)
+            if [[ -z "$2" || "$2" =~ ^- ]]; then
+                echo "Error: --ref requires an argument." >&2
+                exit 1
+            fi
+            DEEPGEMM_GIT_REF="$2"
+            shift 2
+            ;;
+        --cuda-version)
+            if [[ -z "$2" || "$2" =~ ^- ]]; then
+                echo "Error: --cuda-version requires an argument." >&2
+                exit 1
+            fi
+            CUDA_VERSION="$2"
+            shift 2
+            ;;
+        --wheel-dir)
+            if [[ -z "$2" || "$2" =~ ^- ]]; then
+                echo "Error: --wheel-dir requires a directory path." >&2
+                exit 1
+            fi
+            WHEEL_DIR="$2"
+            shift 2
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --ref REF          Git reference to checkout (default: $DEEPGEMM_GIT_REF)"
+            echo "  --cuda-version VER CUDA version (auto-detected if not provided)"
+            echo "  --wheel-dir PATH   If set, build wheel into PATH but do not install"
+            echo "  -h, --help         Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1" >&2
+            exit 1
+            ;;
+    esac
+done
+
+# Auto-detect CUDA version if not provided
+if [ -z "$CUDA_VERSION" ]; then
+    if command -v nvcc >/dev/null 2>&1; then
+        CUDA_VERSION=$(nvcc --version | grep "release" | sed -n 's/.*release \([0-9]\+\.[0-9]\+\).*/\1/p')
+        echo "Auto-detected CUDA version: $CUDA_VERSION"
+    else
+        echo "Warning: Could not auto-detect CUDA version. Please specify with --cuda-version"
+        exit 1
+    fi
+fi
+
+# Extract major and minor version numbers
+CUDA_MAJOR="${CUDA_VERSION%%.*}"
+CUDA_MINOR="${CUDA_VERSION#"${CUDA_MAJOR}".}"
+CUDA_MINOR="${CUDA_MINOR%%.*}"
+echo "CUDA version: $CUDA_VERSION (major: $CUDA_MAJOR, minor: $CUDA_MINOR)"
+
+# Check CUDA version requirement
+if [ "$CUDA_MAJOR" -lt 12 ] || { [ "$CUDA_MAJOR" -eq 12 ] && [ "$CUDA_MINOR" -lt 8 ]; }; then
+    echo "Skipping DeepGEMM build/installation (requires CUDA 12.8+ but got ${CUDA_VERSION})"
+    exit 0
+fi
+
+echo "Preparing DeepGEMM build..."
+echo "Repository: $DEEPGEMM_GIT_REPO"
+echo "Reference: $DEEPGEMM_GIT_REF"
+
+# Create a temporary directory for the build
+INSTALL_DIR=$(mktemp -d)
+trap 'rm -rf "$INSTALL_DIR"' EXIT
+
+# Clone the repository
+git clone --recursive --shallow-submodules "$DEEPGEMM_GIT_REPO" "$INSTALL_DIR/deepgemm"
+pushd "$INSTALL_DIR/deepgemm"
+
+# Checkout the specific reference
+git checkout "$DEEPGEMM_GIT_REF"
+
+# Clean previous build artifacts
+# (Based on https://github.com/deepseek-ai/DeepGEMM/blob/main/install.sh)
+rm -rf -- build dist *.egg-info 2>/dev/null || true
+
+# Build wheel
+echo "🏗️  Building DeepGEMM wheel..."
+python3 setup.py bdist_wheel
+
+# If --wheel-dir was specified, copy wheels there and exit
+if [ -n "$WHEEL_DIR" ]; then
+    mkdir -p "$WHEEL_DIR"
+    cp dist/*.whl "$WHEEL_DIR"/
+    echo "✅ Wheel built and copied to $WHEEL_DIR"
+    popd
+    exit 0
+fi
+
+# Default behaviour: install built wheel
+if command -v uv >/dev/null 2>&1; then
+    echo "Installing DeepGEMM wheel using uv..."
+    uv pip install --system dist/*.whl
+else
+    echo "Installing DeepGEMM wheel using pip..."
+    python3 -m pip install dist/*.whl
+fi
+
+popd
+echo "✅ DeepGEMM installation completed successfully"