[shardformer]: support gpt-j, falcon, Mistral and add interleaved pipeline for bert (#5088)

* [shardformer] implement policy for all GPT-J models and test

* [shardformer] support interleaved pipeline parallel for bert finetune

* [shardformer] shardformer support falcon (#4883)

* [shardformer]: fix interleaved pipeline for bert model (#5048)

* [hotfix]: disable seq parallel for gptj and falcon, and polish code (#5093)

* Add Mistral support for Shardformer (#5103)

* [shardformer] add tests to mistral (#5105)

---------

Co-authored-by: Pengtai Xu <henryxu880@gmail.com>
Co-authored-by: ppt0011 <143150326+ppt0011@users.noreply.github.com>
Co-authored-by: flybird11111 <1829166702@qq.com>
Co-authored-by: eric8607242 <e0928021388@gmail.com>

tests/kit/model_zoo/transformers/mistral.py

@@ -0,0 +1,78 @@
+import torch
+import transformers
+from transformers import MistralConfig
+
+from ..registry import ModelAttribute, model_zoo
+
+# ===============================
+# Register single-sentence Mistral
+# ===============================
+
+
+def data_gen():
+    # Generated from following code snippet
+    #
+    # from transformers import AutoModelForCausalLM, AutoTokenizer
+    # tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+    # input = 'My favourite condiment is vinegar' (last two words repeated to satisfy length requirement)
+    # tokenized_input = tokenizer([input], return_tensors="pt")
+    # input_ids = tokenized_input['input_ids']
+    # attention_mask = tokenized_input['attention_mask']
+    input_ids = torch.tensor([[1, 1984, 16020, 2076, 2487, 349, 21375, 4749]], dtype=torch.int64)
+    attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
+    return dict(input_ids=input_ids, attention_mask=attention_mask)
+
+
+def data_gen_for_lm():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    data["labels"] = data["input_ids"].clone()
+    return data
+
+
+def data_gen_for_sequence_classification():
+    # sequence classification data gen
+    data = data_gen()
+    data["labels"] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+# define output transform function
+output_transform_fn = lambda x: x
+
+# define loss function
+loss_fn_for_mistral_model = lambda x: torch.nn.functional.mse_loss(
+    x.last_hidden_state, torch.ones_like(x.last_hidden_state)
+)
+loss_fn = lambda x: x.loss
+loss_fn_for_seq_classification = lambda output: output.logits.mean()
+
+config = MistralConfig(
+    hidden_size=256, intermediate_size=256, num_attention_heads=64, num_hidden_layers=2, vocab_size=50258
+)
+
+model_zoo.register(
+    name="transformers_mistral",
+    model_fn=lambda: transformers.MistralModel(config),
+    data_gen_fn=data_gen,
+    output_transform_fn=output_transform_fn,
+    loss_fn=loss_fn_for_mistral_model,
+    model_attribute=ModelAttribute(has_control_flow=True),
+)
+model_zoo.register(
+    name="transformers_mistral_for_casual_lm",
+    model_fn=lambda: transformers.MistralForCausalLM(config),
+    data_gen_fn=data_gen_for_lm,
+    output_transform_fn=output_transform_fn,
+    loss_fn=loss_fn,
+    model_attribute=ModelAttribute(has_control_flow=True),
+)
+model_zoo.register(
+    name="transformers_mistral_for_sequence_classification",
+    model_fn=lambda: transformers.MistralForSequenceClassification(config),
+    data_gen_fn=data_gen_for_sequence_classification,
+    output_transform_fn=output_transform_fn,
+    loss_fn=loss_fn_for_seq_classification,
+    model_attribute=ModelAttribute(has_control_flow=True),
+)