# Copyright 2022 The HuggingFace 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. # import math import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester from ...test_modeling_common import ids_tensor if is_torch_available(): import torch from transformers import ( AutoTokenizer, BloomForCausalLM, BloomModel, ) @require_torch class BloomModelTester(CausalLMModelTester): if is_torch_available(): base_model_class = BloomModel def create_and_check_bloom_model_past(self, config, *args): input_ids, _, input_mask, _, _, _ = args model = BloomModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True) outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids)) outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids)) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_bloom_model_attention_mask_past(self, config, *args): input_ids, _, input_mask, _, _, _ = args model = BloomModel(config=config) model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = self.seq_length // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_bloom_model_past_large_inputs(self, config, *args): input_ids, _, input_mask, _, _, _ = args model = BloomModel(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) output, past = outputs.to_tuple() # create hypothetical 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 token_type_ids 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)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[ "last_hidden_state" ] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # 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() # 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_lm_head_model(self, config, *args): input_ids, _, input_mask, _, _, _ = args model = BloomForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_bloom_weight_initialization(self, config, *args): model = BloomModel(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer) for key in model.state_dict(): if "c_proj" in key and "weight" in key: self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001) self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01) @require_torch class BloomModelTest(CausalLMModelTest, unittest.TestCase): model_tester_class = BloomModelTester test_missing_keys = False def test_bloom_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bloom_model_past(*config_and_inputs) def test_bloom_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bloom_model_attention_mask_past(*config_and_inputs) def test_bloom_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bloom_model_past_large_inputs(*config_and_inputs) def test_bloom_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(*config_and_inputs) def test_bloom_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bloom_weight_initialization(*config_and_inputs) @unittest.skip("Bloom needs a 2D attention for alibi") def test_custom_4d_attention_mask(self): pass @require_torch class BloomIntegrationTest(unittest.TestCase): def setUp(self): super().setUp() self.path_bigscience_model = "bigscience/bigscience-small-testing" @require_torch def test_embeddings(self): """ The goal here is to compare the embeddings generated by the model trained using Megatron-LM with the one from the transformers library, with a small GPT2-like model to ensure that the conversion from Megatron-LM to transformers has been done successfully. The script compares the logits of the embedding layer and the transformer layers. WARNING: It is expected that these logits will not have exactly the same statistics when running the code on CPU or GPU. For more info, please visit: - https://github.com/pytorch/pytorch/issues/76052#issuecomment-1103193548 - https://discuss.pytorch.org/t/reproducibility-issue-between-intel-and-amd-cpus/144779/9 You need to install tokenizers following this readme: - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles Tokenizer used during training: - https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles # TODO change the script (or just add skip) when building the env with tokenizers 0.12.0 """ # The config in this checkpoint has `bfloat16` as `dtype` -> model in `bfloat16` model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, dtype="auto") model.eval() EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN = { 3478: 0.0002307891845703125, 368: -0.000568389892578125, 109586: -0.0003910064697265625, 35433: -0.000194549560546875, 2: 0.0004138946533203125, 77: 0.000659942626953125, 132619: -0.00031280517578125, 2175: 0.000457763671875, 23714: 0.000263214111328125, 73173: -0.000286102294921875, 144252: 0.00052642822265625, } EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN = { 3478: -0.00921630859375, 368: -0.010009765625, 109586: -0.01031494140625, 35433: -0.01177978515625, 2: -0.0074462890625, 77: -0.00848388671875, 132619: -0.009521484375, 2175: -0.0074462890625, 23714: -0.0145263671875, 73173: -0.007415771484375, 144252: -0.01007080078125, } EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX = { 3478: 0.0128173828125, 368: 0.01214599609375, 109586: 0.0111083984375, 35433: 0.01019287109375, 2: 0.0157470703125, 77: 0.0174560546875, 132619: 0.0078125, 2175: 0.0113525390625, 23714: 0.0146484375, 73173: 0.01116943359375, 144252: 0.01141357421875, } EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM = {"value": 0.08203125} EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN = { 132619: -0.00031256675720214844, 3478: 0.00023090839385986328, 368: -0.0005702972412109375, 109586: -0.00039124488830566406, 35433: -0.000194549560546875, 2: 0.0004146099090576172, 2175: 0.0004572868347167969, 23714: 0.00026416778564453125, 73173: -0.0002865791320800781, 144252: 0.0005254745483398438, 77: 0.0006618499755859375, } EMBEDDINGS_DS_BEFORE_LN_F_16_MIN = { 3478: -0.00921630859375, 368: -0.010009765625, 109586: -0.01031494140625, 35433: -0.01177978515625, 2: -0.0074462890625, 77: -0.00848388671875, 132619: -0.009521484375, 2175: -0.0074462890625, 23714: -0.0145263671875, 73173: -0.007415771484375, 144252: -0.01007080078125, } EMBEDDINGS_DS_BEFORE_LN_F_16_MAX = { 3478: 0.0128173828125, 368: 0.01214599609375, 109586: 0.0111083984375, 35433: 0.01019287109375, 2: 0.0157470703125, 77: 0.0174560546875, 132619: 0.0078125, 2175: 0.0113525390625, 23714: 0.0146484375, 73173: 0.01116943359375, 144252: 0.01141357421875, } EMBEDDINGS_DS_BEFORE_LN_F_16_SUM = {"value": 0.0821533203125} EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN = { 132619: -0.00031267106533050537, 3478: 0.00023087859153747559, 368: -0.0005701072514057159, 109586: -0.0003911703824996948, 35433: -0.0001944899559020996, 2: 0.0004146844148635864, 2175: 0.00045740045607089996, 23714: 0.0002641640603542328, 73173: -0.0002864748239517212, 144252: 0.0005256589502096176, 77: 0.0006617321632802486, } EMBEDDINGS_DS_BEFORE_LN_F_32_MIN = { 3478: -0.00921630859375, 368: -0.010009765625, 109586: -0.01031494140625, 35433: -0.01177978515625, 2: -0.0074462890625, 77: -0.00848388671875, 132619: -0.009521484375, 2175: -0.0074462890625, 23714: -0.0145263671875, 73173: -0.007415771484375, 144252: -0.01007080078125, } EMBEDDINGS_DS_BEFORE_LN_F_32_MAX = { 3478: 0.0128173828125, 368: 0.01214599609375, 109586: 0.0111083984375, 35433: 0.01019287109375, 2: 0.0157470703125, 77: 0.0174560546875, 132619: 0.0078125, 2175: 0.0113525390625, 23714: 0.0146484375, 73173: 0.01116943359375, 144252: 0.01141357421875, } EMBEDDINGS_DS_BEFORE_LN_F_32_SUM = {"value": 0.08217757940292358} TEST_EMBEDDINGS = { "torch.bfloat16": { "mean": EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN, "max": EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX, "min": EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN, "sum": EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM, }, "torch.float32": { "mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN, "max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX, "min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN, "sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM, }, "torch.float": { "mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN, "max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX, "min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN, "sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM, }, "torch.float16": { "mean": EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN, "max": EMBEDDINGS_DS_BEFORE_LN_F_16_MAX, "min": EMBEDDINGS_DS_BEFORE_LN_F_16_MIN, "sum": EMBEDDINGS_DS_BEFORE_LN_F_16_SUM, }, } EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip EMBEDDINGS_DS_AFTER_LN_MEAN = { 3478: -6.580352783203125e-05, 368: 0.0001316070556640625, 109586: -0.00030517578125, 35433: 4.00543212890625e-05, 2: -7.2479248046875e-05, 77: -8.96453857421875e-05, 132619: 0.0001583099365234375, 2175: 2.1219253540039062e-05, 23714: -0.000247955322265625, 73173: -0.00021839141845703125, 144252: -0.0001430511474609375, } EMBEDDINGS_DS_AFTER_LN_MIN = { 3478: -1.6953125, 368: -1.6875, 109586: -1.6875, 35433: -2.125, 2: -1.390625, 77: -1.5390625, 132619: -1.875, 2175: -1.4609375, 23714: -2.296875, 73173: -1.3515625, 144252: -1.78125, } EMBEDDINGS_DS_AFTER_LN_MAX = { 3478: 2.265625, 368: 2.28125, 109586: 1.953125, 35433: 1.90625, 2: 2.703125, 77: 2.828125, 132619: 1.65625, 2175: 2.015625, 23714: 2.234375, 73173: 2.171875, 144252: 1.828125, } EMBEDDINGS_DS_AFTER_LN = { "mean": EMBEDDINGS_DS_AFTER_LN_MEAN, "min": EMBEDDINGS_DS_AFTER_LN_MIN, "max": EMBEDDINGS_DS_AFTER_LN_MAX, } tensor_ids = torch.LongTensor([EXAMPLE_IDS]) with torch.no_grad(): embeddings = model.transformer.word_embeddings(tensor_ids) embeddings_ln = model.transformer.word_embeddings_layernorm(embeddings) # first check the embeddings before LN output_dict = {"min": {}, "max": {}, "mean": {}, "sum": {"value": embeddings.sum().item()}} for i, idx in enumerate(EXAMPLE_IDS): output_dict["min"][idx] = embeddings.min(dim=-1).values[0][i].item() output_dict["max"][idx] = embeddings.max(dim=-1).values[0][i].item() output_dict["mean"][idx] = embeddings.mean(dim=-1)[0][i].item() for key in TEST_EMBEDDINGS[str(model.dtype)]: self.assertDictEqual(TEST_EMBEDDINGS[str(model.dtype)][key], output_dict[key]) output_dict_norm = {"min": {}, "max": {}, "mean": {}} for i, idx in enumerate(EXAMPLE_IDS): output_dict_norm["min"][idx] = embeddings_ln.min(dim=-1).values[0][i].item() output_dict_norm["max"][idx] = embeddings_ln.max(dim=-1).values[0][i].item() output_dict_norm["mean"][idx] = embeddings_ln.mean(dim=-1)[0][i].item() # This test does not pass when places = 2 for i, key in enumerate(output_dict_norm.keys()): for j, idx in enumerate(output_dict[key].keys()): self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1) @require_torch def test_hidden_states_transformers(self): model = BloomModel.from_pretrained(self.path_bigscience_model, use_cache=False, dtype="auto").to(torch_device) model.eval() EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip MEAN_VALUE_LAST_LM = -4.3392181396484375e-05 MIN_MAX_DICT = {"min": -2.0625, "max": 2.75} tensor_ids = torch.LongTensor([EXAMPLE_IDS]) with torch.no_grad(): logits = model(tensor_ids.to(torch_device)) output_dict = { "min": logits.last_hidden_state.min(dim=-1).values[0][0].item(), "max": logits.last_hidden_state.max(dim=-1).values[0][0].item(), } if torch_device == "cuda": self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=4) else: self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=3) self.assertDictEqual(MIN_MAX_DICT, output_dict) @require_torch def test_logits(self): model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, use_cache=False, dtype="auto").to( torch_device ) # load in bf16 model.eval() EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478] # fmt: skip MEAN_LOGITS_GPU_1 = -1.823902130126953e-05 MEAN_LOGITS_GPU_2 = 1.9431114196777344e-05 tensor_ids = torch.LongTensor([EXAMPLE_IDS]).to(torch_device) with torch.no_grad(): output = model(tensor_ids).logits output_gpu_1, output_gpu_2 = output.split(125440, dim=-1) self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6) self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6) @slow @require_torch_accelerator def test_simple_generation(self): # This test is a bit flaky. For some GPU architectures, pytorch sets by default allow_fp16_reduced_precision_reduction = True and some operations # do not give the same results under this configuration, especially torch.baddmm and torch.bmm. https://pytorch.org/docs/stable/notes/numerical_accuracy.html#fp16-on-mi200 # As we leave the default value (True) for allow_fp16_reduced_precision_reduction, the tests failed when running in half-precision with smaller models (560m) # Please see: https://pytorch.org/docs/stable/notes/cuda.html#reduced-precision-reduction-in-fp16-gemms # This discrepancy is observed only when using small models and seems to be stable for larger models. # Our conclusion is that these operations are flaky for small inputs but seems to be stable for larger inputs (for the functions `baddmm` and `bmm`), and therefore for larger models. # Here is a summary of an ablation study of our observations # EXPECTED_OUTPUT = "I enjoy walking with my cute dog, and I love to watch the kids play. I am a very active person, and I am a very good listener. I am a very good person, and I am a very good person. I am a" # 560m + allow_fp16_reduced_precision_reduction = False + torch.bmm ==> PASS # 560m + allow_fp16_reduced_precision_reduction = False + torch.baddm ==> PASS # 560m + allow_fp16_reduced_precision_reduction = True + torch.baddm ==> PASS # 560m + allow_fp16_reduced_precision_reduction = True + torch.bmm ==> FAIL # EXPECTED_OUTPUT = "I enjoy walking with my cute dog, but I also enjoy hiking, biking, and swimming. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love" # >=1b1 + allow_fp16_reduced_precision_reduction = True + torch.baddm ==> PASS (for use_cache=True and use_cache=False) # >=1b1 + allow_fp16_reduced_precision_reduction = True + torch.bmm ==> PASS # >=1b1 + allow_fp16_reduced_precision_reduction = False + torch.bmm ==> PASS path_560m = "bigscience/bloom-560m" model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device) model = model.eval() tokenizer = AutoTokenizer.from_pretrained(path_560m) input_sentence = "I enjoy walking with my cute dog" # This output has been obtained using fp32 model on the huggingface DGX workstation - NVIDIA A100 GPU EXPECTED_OUTPUT = ( "I enjoy walking with my cute dog, and I love to watch the kids play with the kids. I am a very " "active person, and I enjoy working out, and I am a very active person. I am a very active person, and I" ) input_ids = tokenizer.encode(input_sentence, return_tensors="pt") greedy_output = model.generate(input_ids.to(torch_device), max_length=50) self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT) @slow @require_torch_accelerator def test_batch_generation(self): path_560m = "bigscience/bloom-560m" model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device) model = model.eval() tokenizer = AutoTokenizer.from_pretrained(path_560m, padding_side="left") input_sentence = ["I enjoy walking with my cute dog", "I enjoy walking with my cute dog"] inputs = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) attention_mask = inputs["attention_mask"] greedy_output = model.generate(input_ids, attention_mask=attention_mask, max_length=50, do_sample=False) self.assertEqual( tokenizer.decode(greedy_output[0], skip_special_tokens=True), tokenizer.decode(greedy_output[1], skip_special_tokens=True), ) @slow @require_torch_accelerator def test_batch_generation_padding(self): path_560m = "bigscience/bloom-560m" model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device) model = model.eval() tokenizer = AutoTokenizer.from_pretrained(path_560m, padding_side="left") input_sentence = ["I enjoy walking with my cute dog", "Hello my name is"] input_sentence_without_pad = "Hello my name is" input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True) input_ids_without_pad = tokenizer.encode(input_sentence_without_pad, return_tensors="pt") input_ids, attention_mask = input_ids["input_ids"].to(torch_device), input_ids["attention_mask"] greedy_output = model.generate(input_ids, attention_mask=attention_mask, max_length=50, do_sample=False) greedy_output_without_pad = model.generate( input_ids_without_pad.to(torch_device), max_length=50, do_sample=False ) # test token values self.assertEqual(greedy_output[-1, 3:].tolist(), greedy_output_without_pad[0, :-3].tolist()) # test reconstructions self.assertEqual( tokenizer.decode(greedy_output[-1, 3:], skip_special_tokens=True), tokenizer.decode(greedy_output_without_pad[0, :-3], skip_special_tokens=True), ) @slow @require_torch_accelerator def test_batch_generated_text(self): path_560m = "bigscience/bloom-560m" model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").to(torch_device) model = model.eval() tokenizer = AutoTokenizer.from_pretrained(path_560m, padding_side="left") input_sentences = [ "Hello what is", "Running a quick test with the", ] inputs = tokenizer(input_sentences, return_tensors="pt", padding=True, truncation=True) generated_ids = model.generate( inputs["input_ids"].to(torch_device), attention_mask=inputs["attention_mask"], max_length=20 ) generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) # these generations match those of the PyTorch model EXPECTED_GENERATIONS = [ "Hello what is the best way to get the data from the server? I have tried", "Running a quick test with the following command:\nsudo apt-get install python3\nsudo apt-get install python2", ] self.assertListEqual(generated_text, EXPECTED_GENERATIONS)