refactor: move all llama code to models/llama out of meta reference (#1887)

# What does this PR do?

Move around bits. This makes the copies from llama-models _much_ easier
to maintain and ensures we don't entangle meta-reference specific
tidbits into llama-models code even by accident.

Also, kills the meta-reference-quantized-gpu distro and rolls
quantization deps into meta-reference-gpu.

## Test Plan

```
LLAMA_MODELS_DEBUG=1 \
  with-proxy llama stack run meta-reference-gpu \
  --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct \
   --env INFERENCE_CHECKPOINT_DIR=<DIR> \
   --env MODEL_PARALLEL_SIZE=4 \
   --env QUANTIZATION_TYPE=fp8_mixed
```

Start a server with and without quantization. Point integration tests to
it using:

```
pytest -s -v  tests/integration/inference/test_text_inference.py \
   --stack-config http://localhost:8321 --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```

llama_stack/models/llama/llama4/args.py

@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, model_validator
+
+
+class QuantizationScheme(Enum):
+    int4_weight_int8_dynamic_activation = "int4_weight_int8_dynamic_activation"
+
+
+class QuantizationArgs(BaseModel):
+    scheme: Optional[QuantizationScheme] = None
+    group_size: Optional[int] = None
+    spinquant: bool = False
+
+
+class LoRAArgs(BaseModel):
+    rank: int
+    scale: float
+
+
+class MoEArgs(BaseModel):
+    num_experts: int = -1
+    capacity_factor: float = 1.0  # capacity factor determines how many tokens each expert can choose
+    auto_scale_F: bool = (  # noqa: N815
+        True  # if true, rescales hidden_dim such that number of activated params is same as equivalent dense layer
+    )
+    top_k: int = 1
+    interleave_moe_layer_step: int = 1
+
+
+class Size(BaseModel):
+    height: int
+    width: int
+
+
+class VisionArgs(BaseModel):
+    image_size: Size
+    patch_size: Size
+
+    # parameters for the encoder transformer
+    dim: int
+    n_layers: int
+    n_heads: int
+    mlp_ratio: float
+    output_dim: int
+
+    pixel_shuffle_ratio: float
+
+
+class ModelArgs(BaseModel):
+    dim: int = -1
+    n_layers: int = -1
+    n_heads: int = -1
+    n_kv_heads: Optional[int] = None
+    head_dim: Optional[int] = None
+
+    vocab_size: int = -1
+    multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
+    ffn_dim_multiplier: Optional[float] = None
+    ffn_exp: Optional[float] = None
+    norm_eps: float = 1e-5
+
+    attention_chunk_size: Optional[int] = None
+    rope_theta: float = 500000
+    use_scaled_rope: bool = False
+    nope_layer_interval: Optional[int] = None  # No position encoding in every n layers
+    use_qk_norm: bool = False
+    # Set to True to enable inference-time temperature tuning (useful for very long context)
+    attn_temperature_tuning: bool = False
+    floor_scale: float = 8192.0
+    attn_scale: float = 0.1
+
+    vision_args: Optional[VisionArgs] = None
+    moe_args: Optional[MoEArgs] = None
+    quantization_args: Optional[QuantizationArgs] = None
+    lora_args: Optional[LoRAArgs] = None
+
+    max_batch_size: int = 32
+    max_seq_len: int = 2048
+
+    @model_validator(mode="after")
+    def validate(self) -> "ModelArgs":
+        assert self.n_kv_heads <= self.n_heads, f"n_kv_heads ({self.n_kv_heads}) must be <= n_heads ({self.n_heads})"
+        assert self.n_heads % self.n_kv_heads == 0, (
+            f"n_heads ({self.n_heads}) must be divisible by n_kv_heads ({self.n_kv_heads})"
+        )
+        assert self.dim % self.n_heads == 0, f"dim ({self.dim}) must be divisible by n_heads ({self.n_heads})"
+        return self