transformers/tests/models/cohere2/test_modeling_cohere2.py

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Testing suite for the PyTorch Cohere2 model."""

import unittest

import pytest
from packaging import version
from parameterized import parameterized
from pytest import mark

from transformers import AutoModelForCausalLM, AutoTokenizer, Cohere2Config, is_torch_available, pipeline
from transformers.generation.configuration_utils import GenerationConfig
from transformers.testing_utils import (
    require_flash_attn,
    require_read_token,
    require_torch,
    require_torch_large_gpu,
    slow,
    torch_device,
)

from ...models.cohere.test_modeling_cohere import CohereModelTest, CohereModelTester
from ...test_configuration_common import ConfigTester


if is_torch_available():
    import torch

    from transformers import (
        Cohere2ForCausalLM,
        Cohere2Model,
    )


class Cohere2ModelTester(CohereModelTester):
    config_class = Cohere2Config
    if is_torch_available():
        model_class = Cohere2Model
        for_causal_lm_class = Cohere2ForCausalLM


@require_torch
class Cohere2ModelTest(CohereModelTest, unittest.TestCase):
    all_model_classes = (Cohere2Model, Cohere2ForCausalLM) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "feature-extraction": Cohere2Model,
            "text-generation": Cohere2ForCausalLM,
        }
        if is_torch_available()
        else {}
    )
    _is_stateful = True

    def setUp(self):
        self.model_tester = Cohere2ModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Cohere2Config, hidden_size=37)

    @unittest.skip("Failing because of unique cache (HybridCache)")
    def test_model_outputs_equivalence(self, **kwargs):
        pass

    @unittest.skip("Cohere2's forcefully disables sdpa due to softcapping")
    def test_sdpa_can_dispatch_non_composite_models(self):
        pass

    @unittest.skip("Cohere2's eager attn/sdpa attn outputs are expected to be different")
    def test_eager_matches_sdpa_generate(self):
        pass

    @parameterized.expand([("random",), ("same",)])
    @pytest.mark.generate
    @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
        pass

    @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
    def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type):
        pass

    @pytest.mark.generate
    @unittest.skip("Cohere2 has HybridCache which is not compatible with assisted decoding")
    def test_assisted_decoding_sample(self):
        pass

    @unittest.skip("Cohere2 has HybridCache which is not compatible with dola decoding")
    def test_dola_decoding_sample(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support continue from past kv")
    def test_generate_continue_from_past_key_values(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation")
    def test_contrastive_generate(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation")
    def test_contrastive_generate_dict_outputs_use_cache(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support contrastive generation")
    def test_contrastive_generate_low_memory(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.")
    def test_generate_with_static_cache(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.")
    def test_generate_from_inputs_embeds_with_static_cache(self):
        pass

    @unittest.skip("Cohere2 has HybridCache and doesn't support progressive generation using input embeds.")
    def test_generate_continue_from_inputs_embeds(self):
        pass


@slow
@require_read_token
@require_torch_large_gpu
class Cohere2IntegrationTest(unittest.TestCase):
    input_text = ["Hello I am doing", "Hi today"]
    # This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
    # Depending on the hardware we get different logits / generations
    cuda_compute_capability_major_version = None

    @classmethod
    def setUpClass(cls):
        if is_torch_available() and torch.cuda.is_available():
            # 8 is for A100 / A10 and 7 for T4
            cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]

    def test_model_bf16(self):
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_TEXTS = [
            "<BOS_TOKEN>Hello I am doing a project for a school assignment and I need to create a website for a fictional company. I have",
            "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n",
        ]

        model = AutoModelForCausalLM.from_pretrained(
            model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager"
        ).to(torch_device)

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_fp16(self):
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_TEXTS = [
            "<BOS_TOKEN>Hello I am doing a project for a school assignment and I need to create a website for a fictional company. I have",
            "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n",
        ]

        model = AutoModelForCausalLM.from_pretrained(
            model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16, attn_implementation="eager"
        ).to(torch_device)

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_model_pipeline_bf16(self):
        # See https://github.com/huggingface/transformers/pull/31747 -- pipeline was broken for Cohere2 before this PR
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        # EXPECTED_TEXTS should match the same non-pipeline test, minus the special tokens
        EXPECTED_TEXTS = [
            "Hello I am doing a project for a school assignment and I need to create a website for a fictional company. I have",
            "Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n",
        ]

        model = AutoModelForCausalLM.from_pretrained(
            model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="flex_attention"
        ).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

        output = pipe(self.input_text, max_new_tokens=20, do_sample=False, padding=True)

        self.assertEqual(output[0][0]["generated_text"], EXPECTED_TEXTS[0])
        self.assertEqual(output[1][0]["generated_text"], EXPECTED_TEXTS[1])

    @require_flash_attn
    @mark.flash_attn_test
    def test_model_flash_attn(self):
        # See https://github.com/huggingface/transformers/issues/31953 --- flash attn was generating garbage for Gemma2, especially in long context
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_TEXTS = [
            '<BOS_TOKEN>Hello I am doing a project for my school and I need to create a website for a fictional company. I have the logo and the name of the company. I need a website that is simple and easy to navigate. I need a home page, about us, services, contact us, and a gallery. I need the website to be responsive and I need it to be able to be hosted on a server. I need the website to be done in a week. I need the website to be done in HTML,',
            "<PAD><PAD><BOS_TOKEN>Hi today I'm going to show you how to make a simple and easy to make a chocolate cake.\n\nThis recipe is very simple and easy to make.\n\nYou will need:\n\n* 2 cups of flour\n* 1 cup of sugar\n* 1/2 cup of cocoa powder\n* 1 teaspoon of baking powder\n* 1 teaspoon of baking soda\n* 1/2 teaspoon of salt\n* 2 eggs\n* 1 cup of milk\n",
        ]  # fmt: skip

        model = AutoModelForCausalLM.from_pretrained(
            model_id, attn_implementation="flash_attention_2", torch_dtype="float16"
        ).to(torch_device)
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)

        output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
        output_text = tokenizer.batch_decode(output, skip_special_tokens=False)

        self.assertEqual(output_text, EXPECTED_TEXTS)

    def test_export_static_cache(self):
        if version.parse(torch.__version__) < version.parse("2.5.0"):
            self.skipTest(reason="This test requires torch >= 2.5 to run.")

        from transformers.integrations.executorch import (
            TorchExportableModuleWithStaticCache,
            convert_and_export_with_cache,
        )

        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_TEXT_COMPLETION = [
            "Hello I am doing a project on the effects of social media on mental health. I have a few questions. 1. What is the relationship",
        ]

        tokenizer = AutoTokenizer.from_pretrained(model_id, pad_token="<PAD>", padding_side="right")
        # Load model
        device = "cpu"
        dtype = torch.bfloat16
        cache_implementation = "static"
        attn_implementation = "sdpa"
        batch_size = 1
        model = AutoModelForCausalLM.from_pretrained(
            "CohereForAI/c4ai-command-r7b-12-2024",
            device_map=device,
            torch_dtype=dtype,
            attn_implementation=attn_implementation,
            generation_config=GenerationConfig(
                use_cache=True,
                cache_implementation=cache_implementation,
                max_length=30,
                cache_config={
                    "batch_size": batch_size,
                    "max_cache_len": 30,
                },
            ),
        )

        prompts = ["Hello I am doing"]
        prompt_tokens = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
        prompt_token_ids = prompt_tokens["input_ids"]
        max_new_tokens = 30 - prompt_token_ids.shape[-1]

        # Static Cache + export
        exported_program = convert_and_export_with_cache(model)
        ep_generated_ids = TorchExportableModuleWithStaticCache.generate(
            exported_program=exported_program, prompt_token_ids=prompt_token_ids, max_new_tokens=max_new_tokens
        )
        ep_generated_text = tokenizer.batch_decode(ep_generated_ids, skip_special_tokens=True)
        self.assertEqual(EXPECTED_TEXT_COMPLETION, ep_generated_text)

    @parameterized.expand([("flash_attention_2",), ("sdpa",), ("flex_attention",), ("eager",)])
    @require_read_token
    def test_generation_beyond_sliding_window(self, attn_implementation: str):
        """Test that we can correctly generate beyond the sliding window. This is non trivial as
        we need to correctly slice the attention mask in all cases (because we use a HybridCache).
        Outputs for every attention functions should be coherent and identical.
        """
        model_id = "CohereForAI/c4ai-command-r7b-12-2024"
        EXPECTED_COMPLETIONS = [
            " the mountains, the lakes, the rivers, the waterfalls, the waterfalls, the waterfalls, the waterfalls",
            ", green, yellow, orange, purple, pink, brown, black, white, grey, silver",
        ]

        input_text = [
            "This is a nice place. " * 800 + "I really enjoy the scenery,",  # This is larger than 4096 tokens
            "A list of colors: red, blue",  # This will almost all be padding tokens
        ]
        tokenizer = AutoTokenizer.from_pretrained(model_id, padding="left")
        inputs = tokenizer(input_text, padding=True, return_tensors="pt").to(torch_device)

        model = AutoModelForCausalLM.from_pretrained(
            model_id, attn_implementation=attn_implementation, torch_dtype=torch.float16
        ).to(torch_device)

        # Make sure prefill is larger than sliding window
        input_size = inputs.input_ids.shape[-1]
        self.assertTrue(input_size > model.config.sliding_window)

        out = model.generate(**inputs, max_new_tokens=20)[:, input_size:]
        output_text = tokenizer.batch_decode(out)

        self.assertEqual(output_text, EXPECTED_COMPLETIONS)