[v1] Add support for ShareGPT format. (#9486)

Co-authored-by: frozenleaves <frozen@Mac.local>

src/llamafactory/model/model_utils/moe.py

@@ -14,9 +14,13 @@
 
 from typing import TYPE_CHECKING, Union
 
+import torch
+from torch import nn
+from torch.nn import functional as F
 from transformers.integrations import is_deepspeed_zero3_enabled
 
 from ...extras.misc import check_version
+from ...extras.packages import is_transformers_version_greater_than
 
 
 if TYPE_CHECKING:
@@ -25,6 +29,9 @@ if TYPE_CHECKING:
 
     from ...hparams import ModelArguments
 
+if is_transformers_version_greater_than("4.57.0"):
+    from transformers.models.qwen3_omni_moe import modeling_qwen3_omni_moe
+
 
 def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: list[Union["nn.Module", str]]) -> None:
     check_version("deepspeed>=0.13.0")
@@ -175,3 +182,66 @@ def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_t
 
     elif model_type == "jetmoe":
         setattr(config, "aux_loss_coef", model_args.moe_aux_loss_coef)
+
+
+class Qwen3OmniMoeThinkerTextSparseMoeBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+        self.norm_topk_prob = config.norm_topk_prob
+
+        # gating
+        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+        self.experts = nn.ModuleList(
+            [
+                modeling_qwen3_omni_moe.Qwen3OmniMoeThinkerTextMLP(
+                    config, intermediate_size=config.moe_intermediate_size
+                )
+                for _ in range(self.num_experts)
+            ]
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        # Calculate the routing weights for all experts
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+
+        # Retain the weight of the top_k and reset the rest of the expert rights to 0 (instead of retaining only top_k experts)
+        top_k_weights, top_k_indices = torch.topk(routing_weights, self.top_k, dim=-1)
+        # Initialize the all-zero weight matrix (same shape as all experts)
+        full_routing_weights = torch.zeros_like(routing_weights)
+        # Only the weight of top_k experts is retained, and the weight of the rest of the experts remains at 0
+        full_routing_weights.scatter_(1, top_k_indices, top_k_weights)
+
+        # Normalized top_k weights (keep the original logic consistent)
+        if self.norm_topk_prob:
+            # Calculate the sum of the weights top_k each row (for normalization)
+            top_k_sum = full_routing_weights.sum(dim=-1, keepdim=True)
+            # Avoid dividing by zero
+            top_k_sum = torch.clamp(top_k_sum, min=1e-9)
+            full_routing_weights /= top_k_sum
+
+        # Convert back to the input data type
+        full_routing_weights = full_routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # Go through all the experts (not just the selected ones)
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            # Get the weight of the current expert (inactive expert has a weight of 0 here)
+            expert_weights = full_routing_weights[:, expert_idx, None]  # shape: (batch*seq, 1)
+            # All samples participate in the calculations of the current expert, the weight may be equal to 0
+            current_hidden_states = expert_layer(hidden_states) * expert_weights
+            # Add-up to all expert outputs (experts with a weight of 0 do not affect the result)
+            final_hidden_states += current_hidden_states
+
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits

src/llamafactory/model/patcher.py

@@ -43,10 +43,20 @@ if TYPE_CHECKING:
 
     from ..hparams import ModelArguments
 
+if is_transformers_version_greater_than("4.57.0"):
+    from transformers.models.qwen3_omni_moe import modeling_qwen3_omni_moe
+
 
 logger = logging.get_logger(__name__)
 
 
+def patch_qwen3_omni_moe_thinker_text_sparse_moe_block():
+    if is_transformers_version_greater_than("4.57.0"):
+        from .model_utils.moe import Qwen3OmniMoeThinkerTextSparseMoeBlock
+
+        modeling_qwen3_omni_moe.Qwen3OmniMoeThinkerTextSparseMoeBlock = Qwen3OmniMoeThinkerTextSparseMoeBlock
+
+
 def patch_tokenizer(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> None:
     if "PreTrainedTokenizerBase" not in str(tokenizer._pad.__func__):
         tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
@@ -136,6 +146,9 @@ def patch_config(
     if getattr(config, "model_type", None) == "internlm3" and not is_transformers_version_greater_than("4.47.1"):
         raise RuntimeError("InternLM3 model requires transformers>=4.47.1, please upgrade it.")
 
+    if getattr(config, "model_type", None) == "qwen3_omni_moe":
+        patch_qwen3_omni_moe_thinker_text_sparse_moe_block()
+
     # deepspeed zero3 is not compatible with low_cpu_mem_usage
     init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())
 

src/llamafactory/v1/plugins/data_plugins/converter.py

@@ -15,11 +15,15 @@
 
 from typing import Callable, TypedDict
 
-from typing_extensions import NotRequired
+from typing_extensions import NotRequired, Required
 
+from ....extras import logging
 from ...extras.types import DPOSample, Sample, SFTSample
 
 
+logger = logging.get_logger(__name__)
+
+
 class AlpacaSample(TypedDict, total=False):
     system: NotRequired[str]
     instruction: NotRequired[str]
@@ -27,6 +31,21 @@ class AlpacaSample(TypedDict, total=False):
     output: NotRequired[str]
 
 
+ShareGPTMessage = TypedDict(
+    "ShareGPTMessage",
+    {
+        "from": Required[str],  # Role of the message sender (e.g., "human", "gpt", "system")
+        "value": Required[str],  # Content of the message
+    },
+)
+
+
+class ShareGPTSample(TypedDict, total=False):
+    """Type definition for raw ShareGPT sample."""
+
+    conversations: Required[list[ShareGPTMessage]]
+
+
 class PairSample(TypedDict, total=False):
     prompt: NotRequired[str]
     chosen: NotRequired[list[dict]]
@@ -48,6 +67,20 @@ def alpaca_converter(raw_sample: AlpacaSample) -> SFTSample:
             {"role": "system", "content": [{"type": "text", "value": raw_sample["system"]}], "loss_weight": 0.0}
         )
 
+    if "history" in raw_sample:
+        for idx, item in enumerate(raw_sample["history"]):
+            if len(item) != 2:
+                logger.warning_rank0(
+                    f"Warning: History item at index {idx} has invalid length (expected 2, got {len(item)}). Skipping."
+                )
+                continue
+
+            old_prompt, old_response = item
+            messages.append({"role": "user", "content": [{"type": "text", "value": old_prompt}], "loss_weight": 0.0})
+            messages.append(
+                {"role": "assistant", "content": [{"type": "text", "value": old_response}], "loss_weight": 1.0}
+            )
+
     if "instruction" in raw_sample or "input" in raw_sample:
         messages.append(
             {
@@ -67,6 +100,62 @@ def alpaca_converter(raw_sample: AlpacaSample) -> SFTSample:
     return {"messages": messages}
 
 
+def sharegpt_converter(raw_sample: ShareGPTSample) -> SFTSample:
+    """Converts a raw ShareGPT sample into a formatted SFT (Supervised Fine-Tuning) sample.
+
+    Retains only SFT-relevant scenarios and removes parity checks.
+
+    Args:
+        raw_sample (ShareGPTSample): A raw sample in ShareGPT format.
+
+    Returns:
+        dict: A dictionary containing the formatted 'messages' list for SFT training.
+              Returns an empty list if the input data is invalid.
+    """
+    tag_mapping = {
+        "human": "user",
+        "gpt": "assistant",
+        "observation": "observation",
+        "function_call": "function",
+    }
+    messages = raw_sample.get("conversations", [])
+    aligned_messages = []
+    system_content = ""
+
+    # Extract system message if present (typically the first message)
+    if messages and messages[0]["from"] == "system":
+        system_content = messages[0]["value"]
+        messages = messages[1:]
+
+    if system_content:
+        aligned_messages.append(
+            {"role": "system", "content": [{"type": "text", "value": system_content}], "loss_weight": 0.0}
+        )
+
+    has_invalid_role = False
+    for message in messages:
+        sender = message["from"]
+        # validate sender is in supported tags
+        if sender not in tag_mapping:
+            logger.warning_rank0(f"Unsupported role tag '{sender}' in message: {message}")
+            has_invalid_role = True
+            break
+
+        aligned_messages.append(
+            {
+                "role": tag_mapping[sender],
+                "content": [{"type": "text", "value": message["value"]}],
+                "loss_weight": 0.0 if sender in ("human", "observation") else 1.0,
+            }
+        )
+
+    if has_invalid_role:
+        logger.warning_rank0("Skipping invalid example due to unsupported role tags.")
+        return {"messages": []}
+
+    return {"messages": aligned_messages}
+
+
 def pair_converter(raw_sample: PairSample) -> DPOSample:
     """Convert Pair sample to standard DPO sample.
 
@@ -148,6 +237,7 @@ def pair_converter(raw_sample: PairSample) -> DPOSample:
 CONVERTERS = {
     "alpaca": alpaca_converter,
     "pair": pair_converter,
+    "sharegpt": sharegpt_converter,
 }
 
 

tests_v1/plugins/data_plugins/test_converter.py

@@ -19,6 +19,7 @@ from datasets import load_dataset
 
 from llamafactory.v1.config.data_args import DataArguments
 from llamafactory.v1.core.data_engine import DataEngine
+from llamafactory.v1.plugins.data_plugins.converter import get_converter
 
 
 @pytest.mark.parametrize("num_samples", [16])
@@ -48,6 +49,96 @@ def test_alpaca_converter(num_samples: int):
         assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
 
 
+def test_sharegpt_converter_invalid():
+    example = {
+        "conversations": [
+            {
+                "from": "system",
+                "value": "Processes historical market data to generate trading signals "
+                "based on specified technical indicators.",
+            },
+            {
+                "from": "human",
+                "value": "I possess a detailed dataset, 'Historical_Market_Data.csv'. "
+                "Could you proceed with these function calls to assist me with the task?",
+            },
+            {
+                "from": "gpt",
+                "value": "```tool_call\n{'arguments': '{\"data_file\": \"Historical_Market_Data.csv\"]}', "
+                "'name': 'backtest_trading_signals'}```\n",
+            },
+            {
+                "from": "tool",
+                "value": '<tool id="D2">\n{"analysis": {"RSI_signals": [{"date": "2025-01-10", '
+                '"symbol": "AAPL", "signal": "Buy"}]}}}\n</tool>\n',
+            },
+        ]
+    }
+    dataset_converter = get_converter("sharegpt")
+    assert dataset_converter(example) == {"messages": []}
+
+
+def test_sharegpt_converter_valid():
+    example = {
+        "conversations": [
+            {
+                "from": "system",
+                "value": "Processes historical market data to generate trading signals based on "
+                "specified technical indicators.",
+            },
+            {
+                "from": "human",
+                "value": "I possess a detailed dataset, 'Historical_Market_Data.csv'. "
+                "Could you proceed with these function calls to assist me with the task?",
+            },
+            {
+                "from": "gpt",
+                "value": "```tool_call\n{'arguments': '{\"data_file\": \"Historical_Market_Data.csv\"]}', "
+                "'name': 'backtest_trading_signals'}```\n",
+            },
+        ]
+    }
+    dataset_converter = get_converter("sharegpt")
+    expected_data = {
+        "messages": [
+            {
+                "content": [
+                    {
+                        "type": "text",
+                        "value": "Processes historical market data to generate trading signals based on "
+                        "specified technical indicators.",
+                    }
+                ],
+                "loss_weight": 0.0,
+                "role": "system",
+            },
+            {
+                "content": [
+                    {
+                        "type": "text",
+                        "value": "I possess a detailed dataset, 'Historical_Market_Data.csv'. "
+                        "Could you proceed with these function calls to assist me with the task?",
+                    }
+                ],
+                "loss_weight": 0.0,
+                "role": "user",
+            },
+            {
+                "content": [
+                    {
+                        "type": "text",
+                        "value": "```tool_call\n{'arguments': '{\"data_file\": \"Historical_Market_Data.csv\"]}', "
+                        "'name': 'backtest_trading_signals'}```\n",
+                    }
+                ],
+                "loss_weight": 1.0,
+                "role": "assistant",
+            },
+        ]
+    }
+    assert dataset_converter(example) == expected_data
+
+
 @pytest.mark.parametrize("num_samples", [16])
 def test_pair_converter(num_samples: int):
     data_args = DataArguments(dataset="frozenleaves/tiny-dpo/dataset_info.yaml")
@@ -98,4 +189,6 @@ def test_pair_converter(num_samples: int):
 
 if __name__ == "__main__":
     test_alpaca_converter(1)
+    test_sharegpt_converter_invalid()
+    test_sharegpt_converter_valid()
     test_pair_converter(1)