# coding=utf-8 # Copyright 2025 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. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch FalconH1 model.""" import inspect import unittest import pytest from transformers import FalconH1Config, is_torch_available from transformers.testing_utils import ( require_torch, require_torch_gpu, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, FalconH1ForCausalLM, FalconH1Model from transformers.models.falcon_h1.modeling_falcon_h1 import ( FalconHybridMambaAttentionDynamicCache, ) class FalconH1ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=4, num_attention_heads=4, num_key_value_heads=2, intermediate_size=64, hidden_act="silu", attention_dropout=0.0, attn_layer_indices=None, attn_rotary_emb=8, max_position_embeddings=512, type_vocab_size=16, initializer_range=0.02, num_labels=3, pad_token_id=0, mamba_n_groups=1, mamba_n_heads=16, mamba_d_state=16, mamba_d_conv=4, mamba_expand=2, mamba_chunk_size=16, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.attention_dropout = attention_dropout self.attn_layer_indices = attn_layer_indices self.attn_rotary_emb = attn_rotary_emb self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.num_labels = num_labels self.pad_token_id = pad_token_id self.scope = scope self.mamba_n_groups = mamba_n_groups self.mamba_n_heads = mamba_n_heads self.mamba_d_state = mamba_d_state self.mamba_d_conv = mamba_d_conv self.mamba_expand = mamba_expand self.mamba_chunk_size = mamba_chunk_size def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device)) token_labels = None if self.use_labels: token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) config = self.get_config() return config, input_ids, input_mask, token_labels def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict def get_config(self): # Fix for SDPA tests, force at least 4 layers if self.num_hidden_layers < 4: self.num_hidden_layers = 4 if self.attn_layer_indices is None: d = [x for x in range(2, self.num_hidden_layers) if self.num_hidden_layers % x == 0] if len(d) == 0: raise ValueError("num_hidden_layers is prime, cannot automatically set attn_layer_indices.") d = d[-1] # get the largest divisor self.attn_layer_indices = [x + 1 for x in range(0, self.num_hidden_layers, d)] return FalconH1Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, attention_dropout=self.attention_dropout, attn_layer_indices=self.attn_layer_indices, attn_rotary_emb=self.attn_rotary_emb, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, mamba_n_groups=self.mamba_n_groups, mamba_n_heads=self.mamba_n_heads, mamba_d_state=self.mamba_d_state, mamba_d_conv=self.mamba_d_conv, mamba_expand=self.mamba_expand, mamba_chunk_size=self.mamba_chunk_size, ) def create_and_check_model( self, config, input_ids, input_mask, token_labels, ): model = FalconH1Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, input_mask, token_labels, ): model = FalconH1ForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) result = model(input_ids, attention_mask=input_mask) result = model(input_ids, labels=token_labels) result = model(input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, input_mask, token_labels, ): # config.is_decoder = True # config.add_cross_attention = True model = FalconH1ForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass # Attention: Jamba needs the cache to be initialized to return a cache! past_key_values = FalconHybridMambaAttentionDynamicCache( config, input_ids.shape[0], model.dtype, devices=[model.device for _ in range(model.config.num_hidden_layers)], ) outputs = model( input_ids, attention_mask=input_mask, past_key_values=past_key_values, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, cache_position=torch.arange( input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device ), )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) @require_torch class FalconH1ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (FalconH1Model, FalconH1ForCausalLM) if is_torch_available() else () test_headmasking = False test_pruning = False fx_compatible = False # Need to use `0.8` instead of `0.9` for `test_cpu_offload` # This is because we are hitting edge cases with the causal_mask buffer model_split_percents = [0.5, 0.7, 0.8] pipeline_model_mapping = ( {"feature-extraction": FalconH1Model, "text-generation": FalconH1ForCausalLM} if is_torch_available() else {} ) def setUp(self): self.model_tester = FalconH1ModelTester(self) self.config_tester = ConfigTester(self, config_class=FalconH1Config, hidden_size=64) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_casual_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) # def test_initialization(self): # r""" # Overriding the test_initialization test as the A_log and D params of the FalconH1 mixer are initialized differently # """ # config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # configs_no_init = _config_zero_init(config) # for model_class in self.all_model_classes: # model = model_class(config=configs_no_init) # for name, param in model.named_parameters(): # if param.requires_grad: # if "A_log" in name: # A = torch.arange(1, config.mamba_n_heads + 1, dtype=torch.float32) # torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5) # elif "D" in name: # D = torch.ones(config.mamba_n_heads, dtype=torch.float32) # torch.testing.assert_close(param.data, D, rtol=1e-5, atol=1e-5) # else: # self.assertIn( # ((param.data.mean() * 1e9).round() / 1e9).item(), # [0.0, 1.0], # msg=f"Parameter {name} of model {model_class} seems not properly initialized", # ) def test_mismatched_shapes_have_properly_initialized_weights(self): r""" Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the FalconH1 mixer are initialized differently and we tested that in test_initialization """ self.skipTest(reason="Cumbersome and redundant for FalconH1") def test_attention_outputs(self): r""" Overriding the test_attention_outputs test as the FalconH1 model outputs attention only for its attention layers """ config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) expected_num_attentions = self.model_tester.num_hidden_layers for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) def test_batching_equivalence(self): # need to disable the tril input mask orig = self.model_tester.use_input_mask self.model_tester.use_input_mask = False super().test_batching_equivalence() self.model_tester.use_input_mask = orig # essentially the same test in test_utils, just adjustment for rtol for this model @pytest.mark.generate def test_left_padding_compatibility(self): # NOTE: left-padding results in small numerical differences. This is expected. # See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535 # First, filter out models that don't support left padding # - The model must have generative capabilities if len(self.all_generative_model_classes) == 0: self.skipTest(reason="No generative architecture available for this model.") # - The model must support padding if not self.has_attentions: self.skipTest(reason="This model doesn't support padding.") # - The model must be a decoder-only architecture (encoder-based architectures use right-padding) decoder_only_classes = [] for model_class in self.all_generative_model_classes: config, _ = self.prepare_config_and_inputs_for_generate() if config.is_encoder_decoder: continue else: decoder_only_classes.append(model_class) if len(decoder_only_classes) == 0: self.skipTest(reason="No decoder-only architecture available for this model.") # - Decoder-only architectures derived from encoder-decoder models could support it in theory, but we haven't # added support for it yet. We skip these models for now. has_encoder_attributes = any( attr_name for attr_name in config.to_dict().keys() if attr_name.startswith("encoder") and attr_name != "encoder_no_repeat_ngram_size" ) if has_encoder_attributes: self.skipTest( reason="The decoder-only derived from encoder-decoder models are not expected to support left-padding." ) # Then, test left-padding def _prepare_model_kwargs(input_ids, attention_mask, signature): model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask} if "position_ids" in signature: position_ids = torch.cumsum(attention_mask, dim=-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) model_kwargs["position_ids"] = position_ids if "cache_position" in signature: cache_position = torch.arange(input_ids.shape[-1], device=torch_device) model_kwargs["cache_position"] = cache_position return model_kwargs for model_class in decoder_only_classes: config, inputs_dict = self.prepare_config_and_inputs_for_generate() input_ids = inputs_dict["input_ids"] # - for left padding we absolutely need to use an all ones # attention mask, so we do not use the one in inputs_dict attention_mask = torch.ones_like(input_ids) model = model_class(config).to(torch_device).eval() signature = inspect.signature(model.forward).parameters.keys() # no cache as some models require special cache classes to be init outside forward model.generation_config.use_cache = False # Without padding model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature) next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :] # With left-padding (length 32) # can hardcode pad_token to be 0 as we'll do attn masking anyway pad_token_id = ( config.get_text_config().pad_token_id if config.get_text_config().pad_token_id is not None else 0 ) pad_size = (input_ids.shape[0], 32) padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * pad_token_id padded_input_ids = torch.cat((padding, input_ids), dim=1) padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1) model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature) next_logits_with_padding = model(**model_kwargs).logits[:, -1, :] # They should result in very similar logits torch.testing.assert_close(next_logits_wo_padding, next_logits_with_padding, rtol=1e-5, atol=1e-5) @slow @require_torch @require_torch_gpu class FalconH1ModelIntegrationTest(unittest.TestCase): @slow def test_falcon_h1_hard(self): """ An integration test for Falcon-H1. """ EXPECTED_TEXT = ( "Tell me about the french revolution.\n" "The French Revolution (1789–1799) was a period of radical social and political upheaval in France that " "fundamentally transformed the nation and had profound effects on the rest of Europe and the world. Here are the key aspects of the revolution:\n\n" "### **Causes**\n" "1. **Economic Crisis**: France was in severe financial trouble due to costly wars (particularly the American Revolution), extravagant spending by the monarchy, and inefficient taxation.\n" "2. **Social Inequality**: The rigid class system (the Ancien Régime) divided society into the privileged nobility and clergy (First Estate) and the common people (Third Estate), who bore the brunt of taxation and had few rights.\n" "3. **Enlightenment Ideas**: Philosophers like Rousseau, Voltaire, and Montesquieu inspired ideas of liberty, equality, and popular sovereignty.\n" "4. **Settlement of 1789**: The Estates-General convened to address the financial crisis, leading to the Third Estate's assertion of its rights and the eventual formation of the National Assembly.\n\n" "### **Key Events**\n" "1. **Opening of the Revolution (1789)**:\n" "- **Storming of the Bastille**: Symbolic of the fall of royal tyranny.\n" "- **Declaration of the Rights of Man and of the Citizen**: Proclaimed universal rights to liberty, property, and security.\n" "- **Creation of the National Assembly**: The Third Estate declared itself the representative body of France.\n\n" "2. **Radical Phase (1792–1794)**:\n" "- **Reign of Terror**: Led by Maximilien Robespierre, the Committee of Public Safety enforced radical egalitarianism through the guillotine, executing thousands of perceived enemies of the revolution (monarchists, clergy, aristocrats, and counter-revolutionaries).\n" "- **Execution of Louis XVI**: The king was guillotined in June 1793, symbolizing the end of the monarchy.\n" ) model_id = "tiiuae/Falcon-H1-1.5B-Deep-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = FalconH1ForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") device = "cuda" messages = [{"role": "user", "content": "Tell me about the french revolution."}] input_text = tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) with torch.no_grad(): outputs = model.generate(inputs, max_new_tokens=512, do_sample=False) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) self.assertEqual(generated_text, EXPECTED_TEXT)