# 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 textwrap import unittest import pytest from transformers import DynamicCache, FalconH1Config, is_torch_available from transformers.testing_utils import ( Expectations, require_kernels, require_torch, require_torch_accelerator, 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 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=2, 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 outputs = model( input_ids, attention_mask=input_mask, 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, )["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)) def create_and_check_mamba_chunked_prefill(self, config, input_ids, *args, device="cpu"): """ Adapted from `test_linear_attention_multi_token_cached_forward_matches_single_token` to check whether multi-token cached input is properly handled. Can either be run on GPU (fast path) or CPU (slow path), see `test_mamba_chunked_prefill_*` """ model = FalconH1Model(config=config) model.to(device) model.eval() input_ids = input_ids[:1].to(device) prefill_len = input_ids.shape[1] // 2 + 1 prompt = input_ids[:, :prefill_len] next_token = input_ids[:, prefill_len : prefill_len + 1] distractors = input_ids[:, prefill_len + 1 :] multi_input = torch.cat([next_token, distractors], dim=1) cache_single = DynamicCache(config=config) with torch.no_grad(): model(input_ids=prompt, past_key_values=cache_single, use_cache=True) single_out = model(input_ids=next_token, past_key_values=cache_single, use_cache=True) ref_first = single_out.last_hidden_state[:, 0, :] cache_multi = DynamicCache(config=config) with torch.no_grad(): model(input_ids=prompt, past_key_values=cache_multi, use_cache=True) multi_out = model(input_ids=multi_input, past_key_values=cache_multi, use_cache=True) under_test_first = multi_out.last_hidden_state[:, 0, :] self.parent.assertTrue( torch.allclose(ref_first, under_test_first, atol=1e-4, rtol=1e-4), msg=f"Max diff: {(ref_first - under_test_first).abs().max().item():.6f}", ) def create_and_check_kwargs_reach_mamba2_mixer(self, config, input_ids, *args): """ Kernel kwargs given to the model must reach the Mamba2 mixer, which splats them into the fused conv1d+scan, the conv and the chunk scan. This is how `seq_idx` reaches the kernels for packed / variable-length batches. """ model = FalconH1Model(config=config) model.to(torch_device) model.eval() mixer = model.layers[0].mamba original_forward = mixer.forward seen = [] def recording_forward(*fwd_args, **fwd_kwargs): seen.append(set(fwd_kwargs)) return original_forward(*fwd_args, **fwd_kwargs) mixer.forward = recording_forward input_ids = input_ids.to(torch_device) seq_idx = torch.zeros(input_ids.shape, dtype=torch.int32, device=torch_device) with torch.no_grad(): model(input_ids, seq_idx=seq_idx) self.parent.assertTrue(seen, "the Mamba2 mixer was never called") self.parent.assertIn("seq_idx", seen[0]) @require_torch class FalconH1ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (FalconH1Model, FalconH1ForCausalLM) if is_torch_available() else () # 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 _get_conv_state_shape(self, batch_size: int, config): intermediate_size = ( config.mamba_d_ssm if config.mamba_d_ssm is not None else int(config.mamba_expand * config.hidden_size) ) conv_shape = ( batch_size, intermediate_size + 2 * config.mamba_n_groups * config.mamba_d_state, config.mamba_d_conv, ) return conv_shape def _get_recurrent_state_shape(self, batch_size: int, config): return (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state) 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_causal_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_mamba2_chunked_prefill_cpu(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mamba_chunked_prefill(*config_and_inputs, device="cpu") def test_kwargs_reach_mamba2_mixer(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_kwargs_reach_mamba2_mixer(*config_and_inputs) @require_torch_accelerator @require_kernels def test_mamba2_chunked_prefill_torch_device(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mamba_chunked_prefill(*config_and_inputs, device=torch_device) 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 @pytest.mark.generate def test_left_padding_compatibility(self): # TODO: document why a random attention mask causes this test to fail, but a full mask doesn't unpadded_custom_inputs = {"attention_mask": None} super().test_left_padding_compatibility(unpadded_custom_inputs=unpadded_custom_inputs) @slow @require_torch @require_torch_accelerator class FalconH1ModelIntegrationTest(unittest.TestCase): def test_falcon_h1_hard(self): """ An integration test for Falcon-H1. """ EXPECTED_TEXT_A100 = textwrap.dedent( """\ user Tell me about the french revolution. assistant 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: ### **Causes** 1. **Economic Crisis**: France was in severe financial trouble due to costly wars (particularly the American Revolution), debt, and inefficient taxation. The nobility and clergy were exempt from taxes, while the common people bore the brunt of the burden. 2. **Social Inequality**: French society was divided into three estates: the First Estate (clergy), the Second Estate (nobility), and the Third Estate (commoners, including peasants, bourgeoisie, and urban workers). The disparity between the privileged classes and the common people was immense. 3. **Enlightenment Ideas**: Philosophers like Voltaire, Rousseau, and Montesquieu inspired ideas of liberty, equality, and popular sovereignty, which fueled revolutionary fervor. 4. **Political Instability**: The absolute monarchy under King Louis XVI proved unable to address the country’s problems, leading to widespread discontent. ### **Key Events** 1. **Estates-General (1789)**: The Third Estate broke away from the privileged Estates to form the National Assembly, demanding a constitution and tax reform. 2. **Storming of the Bastille (July 14, 1789)**: A symbol of royal tyranny, the Bastille fortress was stormed by revolutionaries, sparking widespread rebellion. 3. **Declaration of the Rights of Man and of the Citizen (August 1789)**: This foundational document proclaimed liberty, equality, and fraternity. 4. **Reign of Terror (1793–1794)**: Led by Maximilien Robespierre and the Committee of Public Safety, the revolution turned violent as thousands were executed for alleged counter-revolutionary activities. 5. **Rise and Fall of Robespierre (July""" ) EXPECTED_TEXT_A10 = textwrap.dedent( """\ user Tell me about the french revolution. assistant 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: ### **Causes** 1. **Economic Crisis**: France was in severe financial trouble due to costly wars (particularly the American Revolution), debt, and inefficient taxation. 2. **Social Inequality**: The privileged classes (the nobility and clergy) enjoyed vast wealth and power, while the common people (the Third Estate) faced extreme poverty and lacked political representation. 3. **Enlightenment Ideas**: Philosophers like Rousseau, Voltaire, and Montesquieu inspired ideas of liberty, equality, and popular sovereignty. 4. **Settlement of 1789**: The Estates-General convened to address the financial crisis, leading to the Third Estate's declaration of itself as the National Assembly, marking the start of the revolution. ### **Key Events** 1. **Storming of the Bastille (July 14, 1789)**: A symbol of royal tyranny, the Bastille fortress was stormed by revolutionaries, sparking widespread rebellion. 2. **Declaration of the Rights of Man and of the Citizen (1789)**: A foundational document proclaiming liberty, equality, and fraternity. 3. **Reign of Terror (1793–1794)**: Led by Maximilien Robespierre and the Committee of Public Safety, thousands of perceived enemies of the revolution were executed. 4. **Rise and Fall of Robespierre**: Robespierre's radical rule culminated in his own execution during the Thermidorian Reaction (July 1794). 5. **Royalist Restoration (1795)**: The monarchy was restored under Louis XVI, but he was forced to abdicate in favor of Louis XVI's younger brother, Louis XVI. ### **Impact** 1. **End of the""" ) EXPECTED_TEXT_XPU = textwrap.dedent( """\ user Tell me about the french revolution. assistant 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: ### **Causes** 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. 2. **Social Inequality**: The rigid class system (the Ancien Régime) favored the nobility and clergy while the majority of the population (the Third Estate) bore the brunt of taxation and had limited rights. 3. **Enlightenment Ideas**: Philosophers like Rousseau, Voltaire, and Montesquieu inspired ideas of liberty, equality, and popular sovereignty. 4. **Settlement of 1789**: The Estates-General convened to address the financial crisis, leading to debates that exposed the weaknesses of the monarchy and the grievances of the common people. ### **Key Events** 1. **Opening of the Revolution (1789)**: - **Storming of the Bastille**: A symbol of royal tyranny, marking the start of the revolution. - **Declaration of the Rights of Man and of the Citizen**: A foundational document proclaiming liberty, equality, and fraternity. 2. **Stages of the Revolution**: - **Staffords' Reforms (1789–1791)**: Attempts to address grievances, including the abolition of feudal privileges and the introduction of the Civil Constitution of the Church. - **Reign of Terror (1793–1794)**: Led by Maximilien Robespierre, characterized by mass executions of perceived enemies of the revolution, including King Louis XVI and Queen Marie Antoinette. - **Thermidorian Reaction (1794)**: The fall of Robespierre and the end of the Reign of Terror. 3. **""" ) expected_texts = Expectations( { ("cuda", (8, 0)): EXPECTED_TEXT_A100, ("cuda", (8, 6)): EXPECTED_TEXT_A10, ("xpu", None): EXPECTED_TEXT_XPU, } ) EXPECTED_TEXT = expected_texts.get_expectation() model_id = "tiiuae/Falcon-H1-1.5B-Deep-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = FalconH1ForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto") 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(torch_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)