forked from phoenix-oss/llama-stack-mirror
530d4bdfe1
# 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 ```
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# top-level folder for each specific model found within the models/ directory at
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# the top-level of this source tree.
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from typing import Any, Dict, List
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from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear
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from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
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from torch import nn
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from torch.nn import functional as F
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class FeedForward(nn.Module):
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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do_reduce: bool = True,
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):
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super().__init__()
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self.do_reduce = do_reduce
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self.w1 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x)
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self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x)
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self.w3 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x)
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self._register_load_state_dict_pre_hook(self.load_hook)
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def load_hook(
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self,
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state_dict: Dict[str, Any],
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prefix: str,
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local_metadata: Dict[str, Any],
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strict: bool,
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missing_keys: List[str],
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unexpected_keys: List[str],
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error_msgs: List[str],
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) -> None:
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if prefix + "mlp.fc1_weight" in state_dict:
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w1, w3 = state_dict.pop(prefix + "mlp.fc1_weight").chunk(2, dim=0)
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state_dict[prefix + "w1.weight"] = w1
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state_dict[prefix + "w3.weight"] = w3
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state_dict[prefix + "w2.weight"] = state_dict.pop(prefix + "mlp.fc2_weight")
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def forward(self, x):
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x = F.silu(F.linear(x, self.w1.weight)) * F.linear(x, self.w3.weight)
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out = F.linear(x, self.w2.weight)
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if self.do_reduce:
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return reduce_from_model_parallel_region(out)
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return out
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