feat: add zamba2 model pipeline

mindone/transformers/__init__.py

@@ -345,5 +345,6 @@
     XLMRobertaXLModel,
     XLMRobertaXLPreTrainedModel,
 )
+from .models.zamba2 import Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model, Zamba2PreTrainedModel
 from .pipelines import TextGenerationPipeline, pipeline
 from .processing_utils import ProcessorMixin

mindone/transformers/models/auto/configuration_auto.py

@@ -83,6 +83,7 @@
         ("whisper", "WhisperConfig"),
         ("xlm-roberta", "XLMRobertaConfig"),
         ("xlm-roberta-xl", "XLMRobertaXLConfig"),
+        ("zamba2", "Zamba2Config"),
     ]
 )
 
@@ -151,6 +152,7 @@
         ("whisper", "Whisper"),
         ("xlm-roberta", "XLM-RoBERTa"),
         ("xlm-roberta-xl", "XLM-RoBERTa-XL"),
+        ("zamba2", "Zamba2"),
     ]
 )
 

mindone/transformers/models/auto/modeling_auto.py

@@ -82,6 +82,7 @@
         ("whisper", "WhisperModel"),
         ("xlm-roberta", "XLMRobertaModel"),
         ("xlm-roberta-xl", "XLMRobertaXLModel"),
+        ("zamba2", "Zamba2Model"),
     ]
 )
 
@@ -171,6 +172,7 @@
         ("ijepa", "IJepaModel"),
         ("imagegpt", "ImageGPTModel"),
         ("levit", "LevitModel"),
+        ("zamba2", "Zamba2ForCausalLM"),
     ]
 )
 
@@ -326,6 +328,7 @@
         ("t5", "T5ForSequenceClassification"),
         ("umt5", "UMT5ForSequenceClassification"),
         ("xlm-roberta-xl", "XLMRobertaXLForSequenceClassification"),
+        ("zamba2", "Zamba2ForSequenceClassification"),
     ]
 )
 

mindone/transformers/models/zamba2/__init__.py

@@ -0,0 +1 @@
+from .modeling_zamba2 import Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model, Zamba2PreTrainedModel

tests/transformers_tests/models/zamba2/test_modeling_zamba2.py

@@ -0,0 +1,235 @@
+# This module contains test cases that are defined in the `.test_cases.py` file, structured as lists or tuples like
+#     [name, pt_module, ms_module, init_args, init_kwargs, inputs_args, inputs_kwargs, outputs_map].
+#
+# Each defined case corresponds to a pair consisting of PyTorch and MindSpore modules, including their respective
+# initialization parameters and inputs for the forward. The testing framework adopted here is designed to generically
+# parse these parameters to assess and compare the precision of forward outcomes between the two frameworks.
+#
+# In cases where models have unique initialization procedures or require testing with specialized output formats,
+# it is necessary to develop distinct, dedicated test cases.
+
+import inspect
+
+import numpy as np
+import pytest
+import torch
+from transformers import Zamba2Config
+
+import mindspore as ms
+
+from tests.modeling_test_utils import (
+    MS_DTYPE_MAPPING,
+    PT_DTYPE_MAPPING,
+    compute_diffs,
+    generalized_parse_args,
+    get_modules,
+)
+from tests.transformers_tests.models.modeling_common import ids_numpy
+
+DTYPE_AND_THRESHOLDS = {"fp32": 5e-4, "fp16": 5e-3, "bf16": 5e-2}
+MODES = [1]
+
+
+class Zamba2ModelTester:
+    config_class = Zamba2Config
+
+    def __init__(
+        self,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=False,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=54,
+        num_attention_heads=4,
+        num_key_value_heads=2,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        scope=None,
+    ):
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        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.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+        self.head_dim = self.hidden_size // self.num_attention_heads
+
+    # Copied from tests.models.mistral.test_modeling_mistral.MistralModelTester.prepare_config_and_inputs
+    def prepare_config_and_inputs(self):
+        input_ids = ids_numpy([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = np.tril(np.ones_like(input_ids))
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_numpy([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_numpy([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_numpy([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_numpy([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return self.config_class(
+            attn_implementation="eager",
+            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,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+            head_dim=self.head_dim,
+        )
+
+
+model_tester = Zamba2ModelTester()
+(
+    config,
+    input_ids,
+    token_type_ids,
+    input_mask,
+    sequence_labels,
+    token_labels,
+    choice_labels,
+) = model_tester.prepare_config_and_inputs()
+
+
+Zamba2_CASES = [
+    [
+        "Zamba2Model",
+        "transformers.Zamba2Model",
+        "mindone.transformers.Zamba2Model",
+        (config,),
+        {},
+        (input_ids,),
+        {
+            "attention_mask": input_mask,
+        },
+        {
+            "last_hidden_state": 0,
+        },
+    ],
+]
+
+
+# transformers need >= 4.41.2
+@pytest.mark.parametrize(
+    "name,pt_module,ms_module,init_args,init_kwargs,inputs_args,inputs_kwargs,outputs_map,dtype,mode",
+    [
+        case
+        + [
+            dtype,
+        ]
+        + [
+            mode,
+        ]
+        for case in Zamba2_CASES
+        for dtype in DTYPE_AND_THRESHOLDS.keys()
+        for mode in MODES
+    ],
+)
+def test_named_modules(
+    name,
+    pt_module,
+    ms_module,
+    init_args,
+    init_kwargs,
+    inputs_args,
+    inputs_kwargs,
+    outputs_map,
+    dtype,
+    mode,
+):
+    ms.set_context(mode=mode)
+
+    (
+        pt_model,
+        ms_model,
+        pt_dtype,
+        ms_dtype,
+    ) = get_modules(pt_module, ms_module, dtype, *init_args, **init_kwargs)
+    pt_inputs_args, pt_inputs_kwargs, ms_inputs_args, ms_inputs_kwargs = generalized_parse_args(
+        pt_dtype, ms_dtype, *inputs_args, **inputs_kwargs
+    )
+
+    # set `hidden_dtype` if requiring, for some modules always compute in float
+    # precision and require specific `hidden_dtype` to cast before return
+    if "hidden_dtype" in inspect.signature(pt_model.forward).parameters:
+        pt_inputs_kwargs.update({"hidden_dtype": PT_DTYPE_MAPPING[pt_dtype]})
+        ms_inputs_kwargs.update({"hidden_dtype": MS_DTYPE_MAPPING[ms_dtype]})
+    if mode == 0:
+        ms_inputs_kwargs.update({"use_cache": False})
+    with torch.no_grad():
+        pt_outputs = pt_model(*pt_inputs_args, **pt_inputs_kwargs)
+    ms_outputs = ms_model(*ms_inputs_args, **ms_inputs_kwargs)
+    # print("ms:", ms_outputs)
+    # print("pt:", pt_outputs)
+    if outputs_map:
+        pt_outputs_n = []
+        ms_outputs_n = []
+        for pt_key, ms_idx in outputs_map.items():
+            # print("===map", pt_key, ms_idx)
+            pt_output = getattr(pt_outputs, pt_key)
+            ms_output = ms_outputs[ms_idx]
+            if isinstance(pt_output, (list, tuple)):
+                pt_outputs_n += list(pt_output)
+                ms_outputs_n += list(ms_output)
+            else:
+                pt_outputs_n.append(pt_output)
+                ms_outputs_n.append(ms_output)
+        diffs = compute_diffs(pt_outputs_n, ms_outputs_n)
+    else:
+        diffs = compute_diffs(pt_outputs, ms_outputs)
+
+    THRESHOLD = DTYPE_AND_THRESHOLDS[ms_dtype]
+    assert (np.array(diffs) < THRESHOLD).all(), (
+        f"ms_dtype: {ms_dtype}, pt_type:{pt_dtype}, "
+        f"Outputs({np.array(diffs).tolist()}) has diff bigger than {THRESHOLD}"
+    )