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forge/test/models/pytorch/multimodal/deepseek/test_deepseek_math.py
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# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC | ||
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# SPDX-License-Identifier: Apache-2.0 | ||
import pytest | ||
import torch | ||
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig | ||
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import forge | ||
from forge.verify.verify import verify | ||
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from test.models.utils import Framework, Source, Task, build_module_name | ||
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class Wrapper(torch.nn.Module): | ||
def __init__(self, model): | ||
super().__init__() | ||
self.model = model | ||
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def forward(self, input_tensor): | ||
return self.model(input_tensor, max_new_tokens=100).logits | ||
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@pytest.mark.nightly | ||
@pytest.mark.parametrize("variant", ["deepseek-math-7b-instruct"]) | ||
def test_deepseek_math_pytorch(record_forge_property, variant): | ||
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# Build Module Name | ||
module_name = build_module_name( | ||
framework=Framework.PYTORCH, model="deepseek", variant=variant, task=Task.QA, source=Source.HUGGINGFACE | ||
) | ||
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# Record Forge Property | ||
record_forge_property("model_name", module_name) | ||
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model_name = f"deepseek-ai/{variant}" | ||
tokenizer = AutoTokenizer.from_pretrained(model_name) | ||
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") | ||
model.generation_config = GenerationConfig.from_pretrained(model_name) | ||
model.generation_config.pad_token_id = model.generation_config.eos_token_id | ||
framework_model = Wrapper(model) | ||
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messages = [ | ||
{ | ||
"role": "user", | ||
"content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}.", | ||
} | ||
] | ||
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") | ||
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# Forge compile framework model | ||
compiled_model = forge.compile(framework_model, sample_inputs=[input_tensor], module_name=module_name) | ||
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# Model Verification | ||
verify([input_tensor], framework_model, compiled_model) |