wehub-resource-sync
92 lines
3.1 KiB
Python
92 lines
3.1 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 ..common_parameters import *
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from ..layer_container_base import LayerContainer
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'''
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# HF Phi-2 model looks like this:
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PhiForCausalLM(
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(model): PhiModel(
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(embed_tokens): Embedding(51200, 2560)
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(embed_dropout): Dropout(p=0.0, inplace=False)
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(layers): ModuleList(
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(0-31): 32 x PhiDecoderLayer(
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(self_attn): PhiAttention(
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(q_proj): Linear(in_features=2560, out_features=2560, bias=True)
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(k_proj): Linear(in_features=2560, out_features=2560, bias=True)
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(v_proj): Linear(in_features=2560, out_features=2560, bias=True)
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(dense): Linear(in_features=2560, out_features=2560, bias=True)
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(rotary_emb): PhiRotaryEmbedding()
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)
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(mlp): PhiMLP(
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(activation_fn): NewGELUActivation()
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(fc1): Linear(in_features=2560, out_features=10240, bias=True)
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(fc2): Linear(in_features=10240, out_features=2560, bias=True)
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)
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(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
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(resid_dropout): Dropout(p=0.1, inplace=False)
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)
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)
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(final_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
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)
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(lm_head): Linear(in_features=2560, out_features=51200, bias=True)
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)
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'''
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class PhiTransformerContainer(LayerContainer):
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"""
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Transformer layer container for the Phi model.
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"""
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qkv_w: UnfusedQKVParameter
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qkv_b: UnfusedQKVParameter
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attn_out_w: AttentionOutputParameter
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attn_out_b: AttentionOutputParameter
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mlp_1_w: MLP1Parameter
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mlp_1_b: MLP1Parameter
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mlp_2_w: MLP2Parameter
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mlp_2_b: MLP2Parameter
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ln_gamma: NormParameter
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ln_beta: 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.q_proj.bias": "qkv_b.q_params",
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"self_attn.k_proj.bias": "qkv_b.k_params",
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"self_attn.v_proj.bias": "qkv_b.v_params",
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"self_attn.dense.weight": "attn_out_w.params",
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"self_attn.dense.bias": "attn_out_b.params",
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"mlp.fc1.weight": "mlp_1_w.params",
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"mlp.fc1.bias": "mlp_1_b.params",
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"mlp.fc2.weight": "mlp_2_w.params",
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"mlp.fc2.bias": "mlp_2_b.params",
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"input_layernorm.weight": "ln_gamma.params",
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"input_layernorm.bias": "ln_beta.params",
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}
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class PhiNonTransformerContainer(LayerContainer):
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"""
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Non-Transformer layer container for the Phi model.
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"""
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word_emb: EmbeddingParameter
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word_unembed_w: UnembedParameter
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word_unembed_b: UnembedParameter
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final_norm_gamma: NormParameter
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final_norm_beta: NormParameter
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PARAM_MAPPING = {
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"model.embed_tokens.weight": "word_emb.params",
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"model.final_layernorm.weight": "final_norm_gamma.params",
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"model.final_layernorm.bias": "final_norm_beta.params",
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"lm_head.weight": "word_unembed_w.params",
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"lm_head.bias": "word_unembed_b.params",
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}
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