Add training monkeypatches

modeling_qwen3_shared_moe_monkeypatch_liger_cce.py

@@ -0,0 +1,280 @@
+# coding=utf-8
+# Copyright 2025 Charles O. Goddard, The Qwen team, Alibaba Group and 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.
+#
+# The following monkeypatches were applied by Doctor Shotgun:
+#
+# Liger Kernel (https://github.com/linkedin/Liger-Kernel):
+# 1. Liger RMSNorm
+# 2. Liger RoPE
+# 3. Liger SwiGLUMLP
+#
+# Cut Cross-Entropy (https://github.com/apple/ml-cross-entropy):
+# 1. Cut Cross-Entropy
+"""PyTorch Qwen3 model with shared expert support."""
+
+from typing import List, Optional, Union
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+# CCE Patch #
+from cut_cross_entropy.linear_cross_entropy import LCE_IMPL_DEFAULT
+from cut_cross_entropy.transformers.utils import (
+    PatchOptions,
+    apply_lce,
+)
+_PATCH_OPTS = PatchOptions(
+    impl=LCE_IMPL_DEFAULT,
+    reduction="mean",
+    filter_eps="auto",
+    accum_e_fp32=False,
+    accum_c_fp32=False,
+    filter_e_grad=True,
+    filter_c_grad=True,
+    train_only=False,
+)
+# CCE Patch #
+
+# Liger Patch #
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.swiglu import LigerQwen3MoeSwiGLUMLP
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+
+import transformers.models.qwen3_moe.modeling_qwen3_moe
+transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeRMSNorm = LigerRMSNorm
+transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeMLP = LigerQwen3MoeSwiGLUMLP
+transformers.models.qwen3_moe.modeling_qwen3_moe.apply_rotary_pos_emb = liger_rotary_pos_emb
+# Liger Patch #
+
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
+from transformers.activations import ACT2FN
+from transformers.utils import logging
+from transformers.models.mixtral.modeling_mixtral import (
+    load_balancing_loss_func,
+)
+from transformers.models.qwen3_moe.modeling_qwen3_moe import (
+    Qwen3MoeMLP,
+    Qwen3MoeRMSNorm,
+    Qwen3MoeAttention,
+    Qwen3MoeDecoderLayer,
+    Qwen3MoeModel,
+    Qwen3MoeForCausalLM,
+)
+from .configuration_qwen3_shared_moe import Qwen3SharedMoeConfig
+
+import scattermoe
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen3SharedMoeSparseMoeBlock(nn.Module):
+    def __init__(self, config: Qwen3SharedMoeConfig):
+        super().__init__()
+        self.config = config
+        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+        if config.shared_expert_intermediate_size is not None:
+            self.shared_expert = Qwen3MoeMLP(
+                config, intermediate_size=config.shared_expert_intermediate_size
+            )
+        else:
+            self.shared_expert = None
+        self.moe_mlp = scattermoe.mlp.GLUMLP(
+            input_size=self.config.hidden_size,
+            hidden_size=self.config.moe_intermediate_size,
+            num_experts=self.config.num_experts,
+            top_k=self.config.num_experts_per_tok,
+            activation=ACT2FN[config.hidden_act],
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # handling of gate/router logits copied from Qwen3MoeSparseMoeBlock
+        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)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(
+            routing_weights, self.config.num_experts_per_tok, dim=-1
+        )
+        if self.config.norm_topk_prob:  # only diff with mixtral sparse moe block!
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        # modified here to use scattermoe + shared_expert
+        hs_0 = self.moe_mlp(hidden_states, routing_weights, selected_experts)
+
+        if self.shared_expert is not None:
+            shared_res = self.shared_expert(hidden_states)
+            res = hs_0 + shared_res
+        else:
+            res = hs_0
+        res = res.reshape(batch_size, sequence_length, hidden_dim)
+        return res, router_logits
+
+
+class Qwen3SharedMoeDecoderLayer(Qwen3MoeDecoderLayer, nn.Module):
+    def __init__(self, config: Qwen3SharedMoeConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = Qwen3MoeAttention(config, layer_idx)
+
+        if (layer_idx not in config.mlp_only_layers) and (
+            config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
+        ):
+            self.mlp = Qwen3SharedMoeSparseMoeBlock(config)
+        else:
+            self.mlp = Qwen3MoeMLP(config, intermediate_size=config.intermediate_size)
+
+        self.input_layernorm = Qwen3MoeRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = Qwen3MoeRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+
+class Qwen3SharedMoeModel(Qwen3MoeModel):
+    config_class = Qwen3SharedMoeConfig
+
+    def __init__(self, config: Qwen3SharedMoeConfig):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [
+                Qwen3SharedMoeDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+
+class Qwen3SharedMoeForCausalLM(Qwen3MoeForCausalLM):
+    config_class = Qwen3SharedMoeConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen3SharedMoeModel(config)
+        self.num_experts = config.num_experts
+
+    # CCE Patch #
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[list[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs,
+    ) -> MoeCausalLMOutputWithPast:
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_router_logits = (
+            output_router_logits
+            if output_router_logits is not None
+            else self.config.output_router_logits
+        )
+
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: MoeModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_router_logits=output_router_logits,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+
+        if hidden_states is None:
+            raise ValueError("hidden_states is None")
+
+        loss = None
+        logits = None
+
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = (
+            slice(-logits_to_keep, None)
+            if isinstance(logits_to_keep, int)
+            else logits_to_keep
+        )
+
+        if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
+            assert labels is not None
+            loss = apply_lce(
+                hidden_states[:, slice_indices, :],
+                self.lm_head.weight,
+                labels,
+                _PATCH_OPTS,
+                **kwargs,
+            )
+        else:
+            logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+            if labels is not None:
+                loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits,
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(
+                    loss.device
+                )  # make sure to reside in the same device
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+    # CCE Patch #
+

modeling_qwen3_shared_moe_monkeypatch_liger_flce.py

@@ -0,0 +1,279 @@
+# coding=utf-8
+# Copyright 2025 Charles O. Goddard, The Qwen team, Alibaba Group and 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.
+#
+# The following monkeypatches were applied by Doctor Shotgun:
+#
+# Liger Kernel (https://github.com/linkedin/Liger-Kernel):
+# 1. Liger RMSNorm
+# 2. Liger RoPE
+# 3. Liger SwiGLUMLP
+# 4. Liger Fused Linear Cross-Entropy
+"""PyTorch Qwen3 model with shared expert support."""
+
+from typing import List, Optional, Union
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+# Liger Patch #
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.swiglu import LigerQwen3MoeSwiGLUMLP
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+
+import transformers.models.qwen3_moe.modeling_qwen3_moe
+transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeRMSNorm = LigerRMSNorm
+transformers.models.qwen3_moe.modeling_qwen3_moe.Qwen3MoeMLP = LigerQwen3MoeSwiGLUMLP
+transformers.models.qwen3_moe.modeling_qwen3_moe.apply_rotary_pos_emb = liger_rotary_pos_emb
+# Liger Patch #
+
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
+from transformers.activations import ACT2FN
+from transformers.utils import logging
+from transformers.models.mixtral.modeling_mixtral import (
+    load_balancing_loss_func,
+)
+from transformers.models.qwen3_moe.modeling_qwen3_moe import (
+    Qwen3MoeMLP,
+    Qwen3MoeRMSNorm,
+    Qwen3MoeAttention,
+    Qwen3MoeDecoderLayer,
+    Qwen3MoeModel,
+    Qwen3MoeForCausalLM,
+)
+from .configuration_qwen3_shared_moe import Qwen3SharedMoeConfig
+
+import scattermoe
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen3SharedMoeSparseMoeBlock(nn.Module):
+    def __init__(self, config: Qwen3SharedMoeConfig):
+        super().__init__()
+        self.config = config
+        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
+        if config.shared_expert_intermediate_size is not None:
+            self.shared_expert = Qwen3MoeMLP(
+                config, intermediate_size=config.shared_expert_intermediate_size
+            )
+        else:
+            self.shared_expert = None
+        self.moe_mlp = scattermoe.mlp.GLUMLP(
+            input_size=self.config.hidden_size,
+            hidden_size=self.config.moe_intermediate_size,
+            num_experts=self.config.num_experts,
+            top_k=self.config.num_experts_per_tok,
+            activation=ACT2FN[config.hidden_act],
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # handling of gate/router logits copied from Qwen3MoeSparseMoeBlock
+        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)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(
+            routing_weights, self.config.num_experts_per_tok, dim=-1
+        )
+        if self.config.norm_topk_prob:  # only diff with mixtral sparse moe block!
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        # modified here to use scattermoe + shared_expert
+        hs_0 = self.moe_mlp(hidden_states, routing_weights, selected_experts)
+
+        if self.shared_expert is not None:
+            shared_res = self.shared_expert(hidden_states)
+            res = hs_0 + shared_res
+        else:
+            res = hs_0
+        res = res.reshape(batch_size, sequence_length, hidden_dim)
+        return res, router_logits
+
+
+class Qwen3SharedMoeDecoderLayer(Qwen3MoeDecoderLayer, nn.Module):
+    def __init__(self, config: Qwen3SharedMoeConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = Qwen3MoeAttention(config, layer_idx)
+
+        if (layer_idx not in config.mlp_only_layers) and (
+            config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
+        ):
+            self.mlp = Qwen3SharedMoeSparseMoeBlock(config)
+        else:
+            self.mlp = Qwen3MoeMLP(config, intermediate_size=config.intermediate_size)
+
+        self.input_layernorm = Qwen3MoeRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = Qwen3MoeRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+
+class Qwen3SharedMoeModel(Qwen3MoeModel):
+    config_class = Qwen3SharedMoeConfig
+
+    def __init__(self, config: Qwen3SharedMoeConfig):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [
+                Qwen3SharedMoeDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+
+class Qwen3SharedMoeForCausalLM(Qwen3MoeForCausalLM):
+    config_class = Qwen3SharedMoeConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen3SharedMoeModel(config)
+        self.num_experts = config.num_experts
+
+    # Liger Patch #
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        skip_logits: Optional[bool] = None,
+        **kwargs,
+    ) -> MoeCausalLMOutputWithPast:
+        r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+            logits_to_keep (`int` or `torch.Tensor`, *optional*):
+                If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+                This is useful when using packed tensor format (single dimension for batch and sequence length).
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen3MoeForCausalLM
+
+        >>> model = Qwen3MoeForCausalLM.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
+        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: MoeModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_router_logits=output_router_logits,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        kept_hidden_states = hidden_states[:, slice_indices, :]
+
+        shift_labels = kwargs.pop("shift_labels", None)
+        logits = None
+        loss = None
+
+        if skip_logits is None:
+            skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+        if skip_logits:
+            loss = LigerForCausalLMLoss(
+                hidden_states=kept_hidden_states,
+                lm_head_weight=self.lm_head.weight,
+                labels=labels,
+                shift_labels=shift_labels,
+                hidden_size=self.config.hidden_size,
+                **kwargs,
+            )
+        else:  # if in inference model materialize logits
+            logits = self.lm_head(kept_hidden_states)
+            if labels is not None:
+                loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits,
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+    # Liger Patch #
+