support qwen3, dont speak human language

ktransformers/models/custom_modeling_qwen2_moe.py

@@ -0,0 +1,133 @@
+"""
+Date: 2024-11-06 10:05:11
+LastEditors: djw
+LastEditTime: 2024-11-13 07:50:51
+"""
+
+import math
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+from typing import List, Optional, Tuple, Union
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from ktransformers.server.balance_serve.inference.forward_batch import ForwardBatchInput, ForwardBatchOutput
+from ktransformers.models.custom_cache import KGQACache
+from ktransformers.models.modeling_qwen2_moe import Qwen2MoeModel, Qwen2MoePreTrainedModel
+from ktransformers.models.configuration_qwen2_moe import Qwen2MoeConfig
+from ktransformers.operators.flashinfer_batch_prefill_wrapper import flashInferAttn
+
+torch.set_grad_enabled(False)
+torch.set_default_dtype(torch.bfloat16)
+import flashinfer
+
+class KQwen2MoeForCausalLM(Qwen2MoePreTrainedModel):
+
+    cache: KGQACache
+    use_cuda_graph = False
+    def __init__(
+        self,
+        config: Qwen2MoeConfig,
+        cache,
+    ):
+        super().__init__(config)
+        self.model = Qwen2MoeModel(config)
+        self.config = config
+        self.cache = cache
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.attn = [None] * 10
+        
+    def init_wrapper(self, use_cuda_graph, device, max_batch_token, max_batch_size, max_pages, cuda_graph_idx = 0):
+        self.attn[cuda_graph_idx] = flashInferAttn(use_cuda_graph=use_cuda_graph, max_batch_token=max_batch_token, max_batch_size=max_batch_size, max_pages=max_pages, device=device)
+
+
+    def batch_embeddings(self, batch: ForwardBatchInput, device="cuda:0"):
+        features = []
+        for i in range(batch.batch_size):
+            tokens = batch.minibatch.tokens.contiguous()
+            feature = (
+                self.model.embed_tokens(tokens.to(torch.device('cpu')))
+                .to(torch.bfloat16)
+                .to(device=device)
+            )
+            features.append(feature)
+
+        return features
+
+
+    def forward(
+        self,
+        batch: ForwardBatchInput | None = None,
+        features: List[torch.Tensor] | None = None,
+        bsz_tensors: torch.Tensor | None = None,
+        num_tokens_tensors: torch.Tensor | None = None,
+        page_idx: torch.Tensor | None = None,
+        page_offset: torch.Tensor | None = None,
+        cuda_graph_idx: int | None = 0
+    ) -> ForwardBatchOutput:
+        current_stream = torch.cuda.current_stream()
+
+        forward_batch_output = ForwardBatchOutput()
+
+        
+        hidden_states = features[0]
+        self.attn[cuda_graph_idx].calc_batch_indices(hidden_states.shape[0])
+
+        with torch.cuda.stream(current_stream):
+            residual = torch.zeros_like(hidden_states)
+            for i, decode_layer in enumerate(self.model.layers):
+                if self.model.transfer_map is not None and i in self.model.transfer_map:
+                    prev_stream = torch.cuda.current_stream()
+                    cur_device = self.model.transfer_map[i]
+                    if cur_device not in self.model.stream_device_map:
+                        self.model.stream_device_map[cur_device] = torch.cuda.Stream(cur_device)
+                    torch.cuda.set_device(cur_device)
+                    self.model.stream_device_map[cur_device].wait_stream(prev_stream)
+                    torch.cuda.set_stream(self.model.stream_device_map[cur_device])
+                    hidden_states = hidden_states.to(
+                        self.model.transfer_map[i], non_blocking=True
+                    )
+
+                    batch.minibatch.position_ids = (
+                        batch.minibatch.position_ids.to(self.model.transfer_map[i], non_blocking=True)
+                        if batch.minibatch.position_ids is not None
+                        else None
+                    )
+                hidden_states, residual = decode_layer.input_layernorm(hidden_states, num_tokens_tensors, residual)
+                hidden_states = decode_layer.self_attn(hidden_states, self.cache, 
+                                                       position_ids=batch.minibatch.position_ids, 
+                                                       wrapper=self.attn[cuda_graph_idx], bsz_tensors=num_tokens_tensors, 
+                                                       page_idx=page_idx,
+                                                       page_offset=page_offset
+                                                       )
+
+                hidden_states, residual = decode_layer.post_attention_layernorm(hidden_states, num_tokens_tensors, residual)
+                hidden_states = decode_layer.mlp(hidden_states.unsqueeze(0), num_tokens_tensors, cuda_graph_idx)
+                hidden_states = hidden_states.squeeze(0)
+        forward_batch_output = ForwardBatchOutput()
+        with torch.cuda.stream(current_stream):
+            local_logit = self.lm_head(self.model.norm(hidden_states, num_tokens_tensors, residual)[0], num_tokens_tensors)
+            forward_batch_output.logits.append(local_logit)
+
+        return forward_batch_output
+    
+
+               
+    def flash_infer_attn_plan(self, batch: ForwardBatchInput, bsz_tensors, num_tokens_tensors,
+        num_q_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        page_size: int,
+        causal: bool,
+        q_data_type: torch.dtype,
+        kv_data_type: torch.dtype,
+        cuda_graph_idx: int = 0
+        ):
+        minibatch = batch.minibatch
+        self.attn[cuda_graph_idx].plan(minibatch.q_indptr, minibatch.kv_indptr, minibatch.kv_indices, 
+                          minibatch.kv_last_page_len, bsz_tensors, num_tokens_tensors,num_q_heads, num_kv_heads, head_dim, page_size, causal=causal, q_data_type=q_data_type, kv_data_type=kv_data_type)
+