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Minor fixes for Mixtral #2015

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Merged
merged 3 commits into from
Dec 11, 2023
Merged

Minor fixes for Mixtral #2015

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ce4567b
Add mixtral to supported model list
WoosukKwon Dec 11, 2023
2e89251
Minor code cleaning
WoosukKwon Dec 11, 2023
a1ef04d
yapf
WoosukKwon Dec 11, 2023
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3 changes: 3 additions & 0 deletions docs/source/models/supported_models.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,9 @@ Alongside each architecture, we include some popular models that use it.
* - :code:`MistralForCausalLM`
- Mistral, Mistral-Instruct
- :code:`mistralai/Mistral-7B-v0.1`, :code:`mistralai/Mistral-7B-Instruct-v0.1`, etc.
* - :code:`MixtralForCausalLM`
- Mixtral-8x7B, Mixtral-8x7B-Instruct
- :code:`mistralai/Mixtral-8x7B-v0.1`, :code:`mistralai/Mixtral-8x7B-Instruct-v0.1`, etc.
* - :code:`MPTForCausalLM`
- MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter
- :code:`mosaicml/mpt-7b`, :code:`mosaicml/mpt-7b-storywriter`, :code:`mosaicml/mpt-30b`, etc.
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8 changes: 2 additions & 6 deletions vllm/model_executor/models/mixtral.py
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Expand Up @@ -21,7 +21,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Mixtral model."""
from typing import List, Optional, Tuple, Union
from typing import List, Optional, Tuple

import numpy as np

Expand Down Expand Up @@ -453,10 +453,6 @@ def __init__(
assert linear_method is None
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.tok_embeddings: Union[nn.Embedding, None] = None
self.layers: nn.ModuleList = None
self.output: Union[nn.Linear, None] = None
self.sampler: Union[Sampler, None] = None
self.tok_embeddings = VocabParallelEmbedding(
config.vocab_size,
config.hidden_size,
Expand Down Expand Up @@ -492,14 +488,14 @@ def forward(
input_metadata,
cache_event,
)
hidden_states = self.norm(hidden_states)
return hidden_states

def sample(
self,
hidden_states: Optional[torch.Tensor],
sampling_metadata: SamplingMetadata,
) -> SamplerOutput:
hidden_states = self.norm(hidden_states)
next_tokens = self.sampler(self.output.weight, hidden_states,
sampling_metadata)
return next_tokens
Expand Down