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#7535 Update FloatTensor type hints to Tensor (#7883)

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* find & replace all FloatTensors to Tensor

* apply formatting

* Update torch.FloatTensor to torch.Tensor in the remaining files

* formatting

* Fix the rest of the places where FloatTensor is used as well as in documentation

* formatting

* Update new file from FloatTensor to Tensor
This commit is contained in:
Mark Van Aken
2024-05-10 12:53:31 -07:00
committed by GitHub
parent 04f4bd54ea
commit be4afa0bb4
275 changed files with 3765 additions and 3824 deletions
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102
scripts/convert_music_spectrogram_to_diffusers.py
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@@ -17,109 +17,99 @@ MODEL = "base_with_context"
def load_notes_encoder(weights, model):
model.token_embedder.weight = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]))
model.position_encoding.weight = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
)
model.token_embedder.weight = nn.Parameter(torch.Tensor(weights["token_embedder"]["embedding"]))
model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False)
for lyr_num, lyr in enumerate(model.encoders):
ly_weight = weights[f"layers_{lyr_num}"]
lyr.layer[0].layer_norm.weight = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"])
)
lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"]))
attention_weights = ly_weight["attention"]
lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T))
model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"]))
return model
def load_continuous_encoder(weights, model):
model.input_proj.weight = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T))
model.input_proj.weight = nn.Parameter(torch.Tensor(weights["input_proj"]["kernel"].T))
model.position_encoding.weight = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
)
model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False)
for lyr_num, lyr in enumerate(model.encoders):
ly_weight = weights[f"layers_{lyr_num}"]
attention_weights = ly_weight["attention"]
lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
lyr.layer[0].layer_norm.weight = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"])
)
lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"]))
lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"]))
return model
def load_decoder(weights, model):
model.conditioning_emb[0].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T))
model.conditioning_emb[2].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T))
model.conditioning_emb[0].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense0"]["kernel"].T))
model.conditioning_emb[2].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense1"]["kernel"].T))
model.position_encoding.weight = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
)
model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False)
model.continuous_inputs_projection.weight = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T)
torch.Tensor(weights["continuous_inputs_projection"]["kernel"].T)
)
for lyr_num, lyr in enumerate(model.decoders):
ly_weight = weights[f"layers_{lyr_num}"]
lyr.layer[0].layer_norm.weight = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"])
torch.Tensor(ly_weight["pre_self_attention_layer_norm"]["scale"])
)
lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T)
torch.Tensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T)
)
attention_weights = ly_weight["self_attention"]
lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
attention_weights = ly_weight["MultiHeadDotProductAttention_0"]
lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T))
lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T))
lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T))
lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T))
lyr.layer[1].layer_norm.weight = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"])
torch.Tensor(ly_weight["pre_cross_attention_layer_norm"]["scale"])
)
lyr.layer[2].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[2].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
lyr.layer[2].film.scale_bias.weight = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T)
torch.Tensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T)
)
lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T))
model.decoder_norm.weight = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]))
model.decoder_norm.weight = nn.Parameter(torch.Tensor(weights["decoder_norm"]["scale"]))
model.spec_out.weight = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T))
model.spec_out.weight = nn.Parameter(torch.Tensor(weights["spec_out_dense"]["kernel"].T))
return model