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nlpodyssey / cybertron

Cybertron: the home planet of the Transformers in Go
BSD 2-Clause "Simplified" License
280 stars 26 forks source link

cybertron's moka-ai/m3e-base model is inconsistent with Python #26

Open akaler727 opened 1 year ago

akaler727 commented 1 year ago

The results of inference with cybertron's moka-ai/m3e-base model are not consistent with Python, Please tell me, am I missing something and I changed the type of model_max_length from int to float32 because int will report an error and I wonder if this is the problem golang code:

import (
    "context"
    "fmt"

    "github.com/rs/zerolog"
    "github.com/rs/zerolog/log"

    . "github.com/nlpodyssey/cybertron/examples"
    "github.com/nlpodyssey/cybertron/pkg/models/bert"
    "github.com/nlpodyssey/cybertron/pkg/tasks"
    "github.com/nlpodyssey/cybertron/pkg/tasks/textencoding"
)

func main() {
    zerolog.SetGlobalLevel(zerolog.DebugLevel)
    LoadDotenv()

    modelsDir := HasEnvVar("CYBERTRON_MODELS_DIR")
    modelName := HasEnvVar("CYBERTRON_MODEL") // sentence-transformers_all-MiniLM-L6-v2

    m, err := tasks.Load[textencoding.Interface](&tasks.Config{ModelsDir: modelsDir, ModelName: modelName})
    if err != nil {
        log.Fatal().Err(err).Send()
    }
    defer tasks.Finalize(m)
    fn := func(text string, model int) error {
        result, err := m.Encode(context.Background(), text, model) //int(bert.MeanPooling)
        if err != nil {
            return err
        }
        fmt.Printf("%#v - %d\n\n", result.Vector.Data(), result.Vector.Size())
        return nil
    }

    fn("hello", int(bert.MeanPooling))
}

golang results:

Got a connection, launched process /private/var/folders/my/mmkg8gln76j857660h9q908h0000gn/T/___1go_build_github_com_nlpodyssey_cybertron (pid = 92969).
{"level":"debug","file":"moka-ai/moka-ai/m3e-base/config.json","time":"2023-07-27T14:22:35+08:00","message":"model file already exists, skipping download"}
{"level":"debug","file":"moka-ai/moka-ai/m3e-base/pytorch_model.bin","time":"2023-07-27T14:22:35+08:00","message":"model file already exists, skipping download"}
{"level":"debug","file":"moka-ai/moka-ai/m3e-base/vocab.txt","time":"2023-07-27T14:22:35+08:00","message":"model file already exists, skipping download"}
{"level":"debug","file":"moka-ai/moka-ai/m3e-base/tokenizer_config.json","time":"2023-07-27T14:22:35+08:00","message":"model file already exists, skipping download"}
{"level":"info","model":"moka-ai/moka-ai/m3e-base/spago_model.bin","time":"2023-07-27T14:22:35+08:00","message":"model file already exists, skipping conversion"}
float.floatSlice[float32]{0.5586561, 0.3481341, 1.1915668, -0.66031563, -0.14736839, -1.5355552, 0.025558796, 0.101684175, -1.2309126, 0.20468175, 0.9455595, -0.04586415, 0.6733322, -0.4213071, -0.9842657, -0.14411977, 0.2860089, 0.8369727, -0.74724257, -0.8295647, -0.24167839, 0.14310831, -0.95775265, 0.654835, 0.0402015, 0.030705217, -0.2343363, 0.352139, 0.50944626, 0.37679872, 0.9200878, -0.10877298, 0.11220171, -0.07710601, -0.11649196, -0.22657523, 0.7939314, 0.07352792, 0.25212747, 0.50869745, 0.78861177, 0.78612345, -0.87185585, -0.42625034, -0.0033961039, 0.2769445, 0.41774124, 0.15084733, -1.3549421, -0.059750002, 0.5812156, 5.364834, -0.4415004, 0.54449576, -0.060659435, 0.5178813, -0.5485616, -0.4674707, 0.40060604, 0.19186114, -0.20614424, -1.1355268, 0.34049234, -0.33354956, -0.23492238, -0.08065402, -0.047902226, -0.37340134, 1.2953057, -0.9417114, 0.4743478, -1.4645922, -0.91245496, 0.3786436, 0.90022177, -0.4570604, 0.72824585, 0.123488575, 0.26364398, -0.54934716, 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-0.25378853, 0.48217577, 0.25654075, -0.40801167, 0.04489517, 1.046751, -0.726053, -1.4279008, -1.3432583, 0.06791468, -0.051261686, 0.3293378, 0.8105246, -0.3564893, -0.2491971, 0.68325007, -0.14297698, 0.26161194, -0.4075473, 0.39791656, -1.1087991, -0.31646204, -0.13931182, -0.039432134, 0.0050914837, 0.9031293, -1.4465828, -0.9414681, -1.2794018, -1.1000504, -0.7025533} - 768

python code:

def test():

    tokenizer = AutoTokenizer.from_pretrained("moka-ai/m3e-base")
    model = AutoModel.from_pretrained("moka-ai/m3e-base")

    text = "hello"

    inputs = tokenizer(text, return_tensors="pt")
    input_ids = inputs["input_ids"]
    attention_mask = inputs["attention_mask"]

    with torch.no_grad():
    outputs = model(input_ids, attention_mask=attention_mask)

    embeddings = outputs.last_hidden_state[0]
    res = embeddings.numpy()
    print(res)
    print('end')

python results:

[[ 0.55908877  0.37521887  1.0591375  ... -1.2979769  -1.0342392
  -0.7252413 ]
 [ 0.5580415   0.29322627  1.4551864  ... -1.2450337  -1.2312784
  -0.6592587 ]
 [ 0.55908877  0.37521887  1.0591375  ... -1.297977   -1.0342392
  -0.7252413 ]]
matteo-grella commented 1 year ago

Thanks, I’m checking. Back to you asap.