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/ffn.py

@@ -0,0 +1,58 @@
+# 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.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# top-level folder for each specific model found within the models/ directory at
+# the top-level of this source tree.
+
+from typing import Any, Dict, List
+
+from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear
+from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
+from torch import nn
+from torch.nn import functional as F
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        do_reduce: bool = True,
+    ):
+        super().__init__()
+        self.do_reduce = do_reduce
+
+        self.w1 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x)
+        self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x)
+        self.w3 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x)
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(
+        self,
+        state_dict: Dict[str, Any],
+        prefix: str,
+        local_metadata: Dict[str, Any],
+        strict: bool,
+        missing_keys: List[str],
+        unexpected_keys: List[str],
+        error_msgs: List[str],
+    ) -> None:
+        if prefix + "mlp.fc1_weight" in state_dict:
+            w1, w3 = state_dict.pop(prefix + "mlp.fc1_weight").chunk(2, dim=0)
+            state_dict[prefix + "w1.weight"] = w1
+            state_dict[prefix + "w3.weight"] = w3
+            state_dict[prefix + "w2.weight"] = state_dict.pop(prefix + "mlp.fc2_weight")
+
+    def forward(self, x):
+        x = F.silu(F.linear(x, self.w1.weight)) * F.linear(x, self.w3.weight)
+        out = F.linear(x, self.w2.weight)
+        if self.do_reduce:
+            return reduce_from_model_parallel_region(out)
+        return out