FIX: Address final comments for transformers integration (huggingface#13

)

* fix modeling final nits and add proper test file

* for now leave empty tests

* add integration test

* push new test

src/transformers/models/cohere/modeling_cohere.py

@@ -106,23 +106,13 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, s
         self.base = base
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq, persistent=False)
-        # For BC we register cos and sin cached
-        self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-        freqs = torch.outer(t, self.inv_freq)
-        emb = torch.repeat_interleave(freqs, 2, dim=-1)
-        self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False)
-        self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
 
     @torch.no_grad()
-    def forward(self, x, position_ids, seq_len=None):
-        if seq_len is not None:
-            logger.warning_once("The `seq_len` argument is deprecated and unused. It will be removed in v4.39.")
-
+    def forward(self, x, position_ids):
         # x: [bs, num_attention_heads, seq_len, head_size]
         inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
         position_ids_expanded = position_ids[:, None, :].float()
+
         # Force float32 since bfloat16 loses precision on long contexts
         # See https://github.com/huggingface/transformers/pull/29285
         device_type = x.device.type
@@ -1027,9 +1017,11 @@ def _update_causal_mask(self, attention_mask, input_tensor):
         return causal_mask
 
 
+# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with Llama->Cohere
 class CohereForCausalLM(CoherePreTrainedModel):
-    _tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
+    _tied_weights_keys = ["lm_head.weight"]
 
+    # Ignore copy
     def __init__(self, config):
         super().__init__(config)
         self.model = CohereModel(config)
@@ -1058,6 +1050,7 @@ def set_decoder(self, decoder):
     def get_decoder(self):
         return self.model
 
+    # Ignore copy
     @add_start_docstrings_to_model_forward(COHERE_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
     def forward(

src/transformers/utils/fx.py

@@ -135,6 +135,7 @@ def _generate_supported_model_class_names(
     "hubert",
     "layoutlm",
     "llama",
+    "cohere",
     "lxmert",
     "m2m_100",
     "marian",

tests/models/cohere/test_modeling_cohere.py

@@ -20,21 +20,27 @@
 
 from transformers import CohereConfig, is_torch_available
 from transformers.testing_utils import (
+    require_bitsandbytes,
     require_torch,
+    require_torch_multi_gpu,
+    require_torch_sdpa,
     slow,
     torch_device,
 )
 
+from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ids_tensor
+from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
     import torch
 
-    from transformers import CohereForCausalLM, CohereModel
+    from transformers import AutoTokenizer, CohereForCausalLM, CohereModel
 
 
+# Copied from transformers.tests.models.llama.LlamaModelTester with Llama->Cohere
 class CohereModelTester:
     def __init__(
         self,
@@ -109,6 +115,7 @@ def prepare_config_and_inputs(self):
 
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 
+    # Ignore copy
     def get_config(self):
         return CohereConfig(
             vocab_size=self.vocab_size,
@@ -124,6 +131,7 @@ def get_config(self):
             is_decoder=False,
             initializer_range=self.initializer_range,
             pad_token_id=self.pad_token_id,
+            eos_token_id=self.pad_token_id,
         )
 
     def create_and_check_model(
@@ -262,7 +270,7 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_torch
-class CohereModelTest(unittest.TestCase):
+class CohereModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (CohereModel, CohereForCausalLM) if is_torch_available() else ()
     all_generative_model_classes = (CohereForCausalLM,) if is_torch_available() else ()
     pipeline_model_mapping = (
@@ -285,6 +293,14 @@ def setUp(self):
         self.model_tester = CohereModelTester(self)
         self.config_tester = ConfigTester(self, config_class=CohereConfig, hidden_size=37)
 
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip("TODO @gante fix this for Cohere")
+    @parameterized.expand([(1, False), (1, True), (4, False)])
+    def test_new_cache_format(self, num_beams, do_sample):
+        pass
+
     def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
@@ -295,26 +311,112 @@ def test_model_various_embeddings(self):
             config_and_inputs[0].position_embedding_type = type
             self.model_tester.create_and_check_model(*config_and_inputs)
 
-    @unittest.skip("TODO @gante fix this")
-    @parameterized.expand([(1, False), (1, True), (4, False)])
-    def test_new_cache_format(self, num_beams, do_sample):
-        pass
+    @require_bitsandbytes
+    @require_torch_sdpa
+    @require_torch_multi_gpu
+    @slow
+    def test_eager_matches_sdpa_generate(self):
+        """
+        Overwritting the common test as the test is flaky on tiny models
+        """
+        max_new_tokens = 30
+
+        model_id = "CohereForAI/c4ai-command-r-v01-4bit"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+        model_sdpa = CohereForCausalLM.from_pretrained(
+            model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto"
+        )
+        self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
+
+        model_eager = CohereForCausalLM.from_pretrained(
+            model_id, torch_dtype=torch.float16, attn_implementation="eager", device_map="auto"
+        )
+
+        self.assertTrue(model_eager.config._attn_implementation == "eager")
+
+        for name, submodule in model_eager.named_modules():
+            if "SdpaAttention" in submodule.__class__.__name__:
+                raise ValueError("The eager model should not have SDPA attention layers")
+
+        has_sdpa = False
+        for name, submodule in model_sdpa.named_modules():
+            if "SdpaAttention" in submodule.__class__.__name__:
+                has_sdpa = True
+                break
+        if not has_sdpa:
+            raise ValueError("The SDPA model should have SDPA attention layers")
+
+        texts = [
+            "hi here's a longer context, getting longer and",
+            "Hello this is a very long sentence my friend, very long for real",
+            "Today I am in Paris and",
+        ]
+
+        for padding_side in ["left", "right"]:
+            tokenizer.padding_side = padding_side
+            tokenizer.pad_token = tokenizer.eos_token
+
+            inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device)
+
+            res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+            res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+
+            with self.subTest(f"{padding_side}"):
+                torch.testing.assert_close(
+                    res_eager,
+                    res_sdpa,
+                    msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}",
+                )
 
 
 @require_torch
+@slow
 class CohereIntegrationTest(unittest.TestCase):
-    @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!")
-    @slow
-    def test_model_logits(self):
-        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
-        model = CohereForCausalLM.from_pretrained("CohereForAI/c4ai-command-r-v01", device_map="auto")
-        out = model(torch.tensor(input_ids).unsqueeze(0))
-        # # Expected mean on dim = -1
-        EXPECTED_MEAN = torch.tensor([[0.5077, -2.5771, -1.1590, -2.6220, -1.7837, -2.4421, -1.3293, -2.2028]])
-        torch.testing.assert_close(out[0].mean(-1).cpu(), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
-        # slicing logits[0, 0, 0:30]
-        EXPECTED_SLICE = torch.tensor([ 1.8525,  5.0039,  2.7734,  3.6270,  0.9390, -0.4587,  3.4062,  0.9468, \
-                3.7324,  1.2344,  5.3047,  4.7266,  5.9414,  5.5195,  1.8047,  3.5215, \
-                1.5752,  3.7031,  6.2891,  3.4785,  2.0293,  4.2539,  2.8086,  4.7070, \
-                3.6953,  4.0391,  3.9766,  3.3066,  2.9395,  3.3105])  # fmt: skip
-        torch.testing.assert_close(out[0][0, 0, :30].cpu(), EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
+    @require_torch_multi_gpu
+    def test_batched_4bit(self):
+        model_id = "CohereForAI/c4ai-command-r-v01-4bit"
+
+        EXPECTED_TEXT = [
+            'Hello today I am going to show you how to make a simple and easy card using the new stamp set called "Hello" from the Occasions catalog. This set is so versatile and can be used for many occasions. I used the new In',
+            "Hi there, here we are again with another great collection of free fonts. This time we have gathered 10 free fonts that you can download and use in your designs. These fonts are free for personal and commercial use. So",
+        ]
+
+        model = CohereForCausalLM.from_pretrained(model_id)
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+        tokenizer.pad_token = tokenizer.eos_token
+
+        text = ["Hello today I am going to show you how to", "Hi there, here we are"]
+        inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        self.assertEqual(tokenizer.batch_decode(output, skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_batched_small_model_logits(self):
+        # Since the model is very large, we created a random cohere model so that we can do a simple
+        # logits check on it.
+        model_id = "hf-internal-testing/cohere-random"
+
+        EXPECTED_LOGITS = torch.Tensor(
+            [
+                [[0.0000, 0.1866, -0.1997], [0.0000, -0.0736, 0.1785], [0.0000, -0.1965, -0.0569]],
+                [[0.0000, -0.0302, 0.1488], [0.0000, -0.0402, 0.1351], [0.0000, -0.0341, 0.1116]],
+            ]
+        ).to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = CohereForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to(
+            torch_device
+        )
+
+        tokenizer.pad_token = tokenizer.eos_token
+
+        text = ["Hello today I am going to show you how to", "Hi there, here we are"]
+        inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
+
+        with torch.no_grad():
+            output = model(**inputs)
+
+        logits = output.logits
+        self.assertTrue(torch.allclose(EXPECTED_LOGITS, logits[:, :3, :3], rtol=1e-3, atol=1e-3))