wudixzy stack docker build test: 2615-line qwen3_5.py + 12 prebuilt .so + 251-line patch_ops.sh

qwen3_6_scripts/qwen3_5/__init__.py

@@ -0,0 +1,3 @@
+from .configuration_qwen3_5 import Qwen3_5Config, Qwen3_5TextConfig, Qwen3_5VisionConfig
+
+__all__ = ["Qwen3_5Config", "Qwen3_5TextConfig", "Qwen3_5VisionConfig"]

qwen3_6_scripts/qwen3_5/configuration_qwen3_5.py

@@ -0,0 +1,242 @@
+# Adapted from transformers 5.2.0 for compatibility with transformers 4.55.3 + torch 2.1.0
+# Stubs layer_type_validation and RopeParameters which do not exist in 4.55.3
+
+import os
+from typing import Optional, List
+
+from ...configuration_utils import PretrainedConfig as PreTrainedConfig
+
+# --- Local stubs for APIs not present in transformers 4.55.3 ---
+# Always use these definitions; do NOT import from the older transformers
+# as same-named functions there have incompatible signatures.
+
+def layer_type_validation(layer_types, num_hidden_layers=None, attention=True):
+    allowed = {"full_attention", "linear_attention"}
+    if not all(lt in allowed for lt in layer_types):
+        raise ValueError(f"layer_types entries must be in {allowed}, got {layer_types}")
+    if num_hidden_layers is not None and num_hidden_layers != len(layer_types):
+        raise ValueError(
+            f"num_hidden_layers ({num_hidden_layers}) != len(layer_types) ({len(layer_types)})"
+        )
+
+
+HYBRID_KV_ACCOUNTING_ENV = "BI100_HYBRID_KV_ACCOUNTING"
+HYBRID_KV_ACCOUNTING_CONFIG = "bi100_hybrid_kv_accounting_mode"
+LEGACY_KV_ACCOUNTING = "legacy40"
+FULL_ATTENTION_KV_ACCOUNTING = "full_attention"
+
+
+def _hybrid_kv_accounting_mode(environ=None, serialized_mode=None):
+    source = os.environ if environ is None else environ
+    environment_mode = source.get(HYBRID_KV_ACCOUNTING_ENV)
+    if (environment_mode is not None and serialized_mode is not None
+            and environment_mode != serialized_mode):
+        raise RuntimeError(
+            f"{HYBRID_KV_ACCOUNTING_ENV}={environment_mode!r} conflicts "
+            f"with serialized {HYBRID_KV_ACCOUNTING_CONFIG}="
+            f"{serialized_mode!r}")
+    mode = environment_mode or serialized_mode or LEGACY_KV_ACCOUNTING
+    if mode not in (LEGACY_KV_ACCOUNTING, FULL_ATTENTION_KV_ACCOUNTING):
+        raise RuntimeError(
+            f"{HYBRID_KV_ACCOUNTING_ENV} must be "
+            f"'{LEGACY_KV_ACCOUNTING}' or "
+            f"'{FULL_ATTENTION_KV_ACCOUNTING}', got {mode!r}")
+    return mode
+
+
+def _vllm_layers_block_type(
+    layer_types,
+    environ=None,
+    serialized_mode=None,
+):
+    """Expose hybrid-layer ownership in the form vLLM 0.6.3 consumes."""
+    mode = _hybrid_kv_accounting_mode(environ, serialized_mode)
+    if mode == LEGACY_KV_ACCOUNTING:
+        return ["attention"] * len(layer_types)
+    return [
+        "attention" if layer_type == "full_attention" else layer_type
+        for layer_type in layer_types
+    ]
+
+try:
+    from typing import TypedDict
+except ImportError:
+    RopeParameters = dict
+else:
+    class RopeParameters(TypedDict, total=False):
+        rope_theta: float
+        rope_type: str
+        partial_rotary_factor: float
+        factor: float
+
+# --- End stubs ---
+
+
+class Qwen3_5TextConfig(PreTrainedConfig):
+    r"""
+    Configuration for the text backbone of Qwen3.5 / Qwen3.6-35B-A3B models.
+    model_type is "qwen3_5_text" (used internally by the nested config).
+    """
+
+    model_type = "qwen3_5_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=248320,
+        hidden_size=4096,
+        intermediate_size=12288,
+        num_hidden_layers=32,
+        num_attention_heads=16,
+        num_key_value_heads=4,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_parameters=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        head_dim=256,
+        linear_conv_kernel_dim=4,
+        linear_key_head_dim=128,
+        linear_value_head_dim=128,
+        linear_num_key_heads=16,
+        linear_num_value_heads=32,
+        layer_types=None,
+        pad_token_id=None,
+        bos_token_id=None,
+        eos_token_id=None,
+        **kwargs,
+    ):
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = 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.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
+        self.rope_parameters = rope_parameters
+        kwargs.setdefault("partial_rotary_factor", 0.25)
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            interval_pattern = kwargs.get("full_attention_interval", 4)
+            self.layer_types = [
+                "linear_attention" if bool((i + 1) % interval_pattern) else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+        self.linear_conv_kernel_dim = linear_conv_kernel_dim
+        self.linear_key_head_dim = linear_key_head_dim
+        self.linear_value_head_dim = linear_value_head_dim
+        self.linear_num_key_heads = linear_num_key_heads
+        self.linear_num_value_heads = linear_num_value_heads
+        super().__init__(**kwargs)
+
+
+class Qwen3_5VisionConfig(PreTrainedConfig):
+    model_type = "qwen3_5_vision"
+
+    def __init__(
+        self,
+        depth=27,
+        hidden_size=1152,
+        hidden_act="gelu_pytorch_tanh",
+        intermediate_size=4304,
+        num_heads=16,
+        in_channels=3,
+        patch_size=16,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        out_hidden_size=3584,
+        num_position_embeddings=2304,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.out_hidden_size = out_hidden_size
+        self.num_position_embeddings = num_position_embeddings
+        self.initializer_range = initializer_range
+
+
+class Qwen3_5Config(PreTrainedConfig):
+    r"""
+    Top-level configuration for Qwen3.5 / Qwen3.6-35B-A3B.
+    model_type = "qwen3_5" matches the model card / config.json.
+    Wraps Qwen3_5TextConfig (and optionally Qwen3_5VisionConfig for multimodal use).
+    For vLLM text-only inference only text_config is consumed.
+    """
+
+    model_type = "qwen3_5"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        image_token_id=248056,
+        video_token_id=248057,
+        vision_start_token_id=248053,
+        vision_end_token_id=248054,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        serialized_mode = kwargs.pop(HYBRID_KV_ACCOUNTING_CONFIG, None)
+        serialized_layers = kwargs.pop("layers_block_type", None)
+        if isinstance(text_config, dict):
+            self.text_config = Qwen3_5TextConfig(**text_config)
+        elif text_config is None:
+            self.text_config = Qwen3_5TextConfig()
+        else:
+            self.text_config = text_config
+
+        if isinstance(vision_config, dict):
+            self.vision_config = Qwen3_5VisionConfig(**vision_config)
+        elif vision_config is None:
+            self.vision_config = Qwen3_5VisionConfig()
+        else:
+            self.vision_config = vision_config
+
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(**kwargs)
+        mode = _hybrid_kv_accounting_mode(
+            serialized_mode=serialized_mode)
+        layers_block_type = _vllm_layers_block_type(
+            self.text_config.layer_types, serialized_mode=mode)
+        if (serialized_layers is not None
+                and list(serialized_layers) != layers_block_type):
+            raise RuntimeError(
+                "serialized layers_block_type conflicts with "
+                f"{HYBRID_KV_ACCOUNTING_CONFIG}={mode!r}")
+        setattr(self, HYBRID_KV_ACCOUNTING_CONFIG, mode)
+        self.layers_block_type = layers_block_type
+
+
+__all__ = ["Qwen3_5Config", "Qwen3_5TextConfig", "Qwen3_5VisionConfig"]