wehub-resource-sync
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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# Create a container object to save model-specific tensors using the policy file above.
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from deepspeed.inference.v2.model_implementations.common_parameters import *
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from deepspeed.inference.v2.model_implementations.layer_container_base import LayerContainer
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'''
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# HF Mistral model (mistralai/Mistral-7B-v0.1) looks like this:
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MistralForCausalLM(
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(model): MistralModel(
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(embed_tokens): Embedding(32000, 4096)
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(layers): ModuleList(
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(0-31): 32 x MistralDecoderLayer(
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(self_attn): MistralAttention(
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(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
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(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
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(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
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(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
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(rotary_emb): MistralRotaryEmbedding()
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)
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(mlp): MistralMLP(
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(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
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(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
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(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
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(act_fn): SiLUActivation()
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)
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(input_layernorm): MistralRMSNorm()
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(post_attention_layernorm): MistralRMSNorm()
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)
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)
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(norm): MistralRMSNorm()
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)
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(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
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)
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'''
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class MistralTransformerContainer(LayerContainer):
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"""
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Transformer layer container for the Mistral model.
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"""
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qkv_w: UnfusedQKVParameter
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attn_out_w: AttentionOutputParameter
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mlp_1_w: GatedMLPParameter
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mlp_2_w: MLP2Parameter
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attn_norm_gamma: NormParameter
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mlp_norm_gamma: NormParameter
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PARAM_MAPPING = {
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"self_attn.q_proj.weight": "qkv_w.q_params",
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"self_attn.k_proj.weight": "qkv_w.k_params",
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"self_attn.v_proj.weight": "qkv_w.v_params",
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"self_attn.o_proj.weight": "attn_out_w.params",
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"mlp.gate_proj.weight": "mlp_1_w.gate_params",
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"mlp.up_proj.weight": "mlp_1_w.up_params",
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"mlp.down_proj.weight": "mlp_2_w.params",
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"input_layernorm.weight": "attn_norm_gamma.params",
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"post_attention_layernorm.weight": "mlp_norm_gamma.params",
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}
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class MistralNonTransformerContainer(LayerContainer):
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"""
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Non-Transformer layer container for the Mistral model.
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"""
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word_emb: EmbeddingParameter
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word_unembed: UnembedParameter
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final_norm: NormParameter
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PARAM_MAPPING = {
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"model.embed_tokens.weight": "word_emb.params",
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"model.norm.weight": "final_norm.params",
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"lm_head.weight": "word_unembed.params",
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}
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