[xpu] support mtp for xpu(mix)

fastdeploy/model_executor/layers/sample/ops/apply_penalty_multi_scores.py

@@ -182,24 +182,28 @@ def apply_speculative_penalty_multi_scores(
         from fastdeploy.model_executor.ops.gpu import (
             speculate_get_token_penalty_multi_scores,
         )
-
-        speculate_get_token_penalty_multi_scores(
-            pre_token_ids,
-            logits,
-            repetition_penalties,
-            frequency_penalties,
-            presence_penalties,
-            temperature,
-            bad_words_token_ids,
-            step_idx,
-            min_dec_lens,
-            eos_token_ids,
-            seq_lens_this_time,
-            output_padding_offset,
-            output_cum_offsets,
-            max_len,
+    elif current_platform.is_xpu():
+        from fastdeploy.model_executor.ops.xpu import (
+            speculate_get_token_penalty_multi_scores,
         )
+
     else:
         raise NotImplementedError
+    speculate_get_token_penalty_multi_scores(
+        pre_token_ids,
+        logits,
+        repetition_penalties,
+        frequency_penalties,
+        presence_penalties,
+        temperature,
+        bad_words_token_ids,
+        step_idx,
+        min_dec_lens,
+        eos_token_ids,
+        seq_lens_this_time,
+        output_padding_offset,
+        output_cum_offsets,
+        max_len,
+    )
     # inplace
     return logits

fastdeploy/model_executor/layers/sample/sampler.py

@@ -572,6 +572,8 @@ def __init__(self, fd_config: FDConfig):
         super().__init__()
         if current_platform.is_cuda():
             self.forward = self.forward_cuda
+        elif current_platform.is_xpu():
+            self.forward = self.forward_xpu
         else:
             raise NotImplementedError
         self.logprobs_mode = fd_config.model_config.logprobs_mode
@@ -814,6 +816,80 @@ def forward_cuda(
 
         return sampler_output
 
+    def forward_xpu(
+        self,
+        logits: paddle.Tensor,
+        sampling_metadata: SamplingMetadata,
+        max_model_len: int,
+        share_inputs: List[paddle.Tensor],
+        accept_all_drafts: bool = False,
+        reject_all_drafts: bool = False,
+    ) -> paddle.Tensor:
+        from fastdeploy.model_executor.ops.xpu import speculate_verify, top_p_candidates
+
+        logits = apply_speculative_penalty_multi_scores(
+            sampling_metadata.pre_token_ids,
+            logits,
+            sampling_metadata.repetition_penalties,
+            sampling_metadata.frequency_penalties,
+            sampling_metadata.presence_penalties,
+            sampling_metadata.temperature,
+            sampling_metadata.bad_words_token_ids,
+            sampling_metadata.step_idx,
+            sampling_metadata.min_dec_lens,
+            sampling_metadata.eos_token_ids,
+            share_inputs["seq_lens_this_time"],
+            share_inputs["output_padding_offset"],
+            share_inputs["output_cum_offsets"],
+            max_model_len,
+        )
+
+        probs = F.softmax(logits)
+
+        verify_scores, verify_tokens, actual_candidate_len = top_p_candidates(
+            probs,
+            sampling_metadata.top_p,
+            share_inputs["output_padding_offset"],
+            self.speculative_max_candidate_len,
+            max_model_len,
+        )
+
+        speculate_verify(
+            share_inputs["accept_tokens"],
+            share_inputs["accept_num"],
+            share_inputs["step_idx"],
+            share_inputs["stop_flags"],
+            share_inputs["seq_lens_encoder"],
+            share_inputs["seq_lens_decoder"],
+            share_inputs[
+                "draft_tokens"
+            ],  # Both input and output, need to write the last 1 token accepted to position 0.
+            share_inputs["seq_lens_this_time"],
+            verify_tokens,
+            verify_scores,
+            share_inputs["max_dec_len"],
+            sampling_metadata.eos_token_ids,
+            share_inputs["is_block_step"],
+            share_inputs["output_cum_offsets"],
+            actual_candidate_len,
+            share_inputs["actual_draft_token_num"],
+            sampling_metadata.top_p,
+            max_model_len,
+            self.speculative_verify_window,
+            True,  # enable_topp
+            (self.speculative_benchmark_mode or reject_all_drafts),
+            accept_all_drafts,
+        )
+        # TODO(chenhuan09): support return logprobs
+        token_ids = share_inputs["accept_tokens"]
+        sampler_output = SamplerOutput(
+            sampled_token_ids=token_ids,
+            logprobs_tensors=None,
+            token_num_per_batch=share_inputs["accept_num"],
+            cu_batch_token_offset=None,
+        )
+        return sampler_output
+
 
 class MTPSampler(nn.Layer):
     """ """
@@ -823,6 +899,8 @@ def __init__(self, fd_config: FDConfig):
         super().__init__()
         if current_platform.is_cuda():
             self.forward = self.forward_cuda
+        elif current_platform.is_xpu():
+            self.forward = self.forward_xpu
         else:
             raise NotImplementedError
         self.logprobs_mode = fd_config.model_config.logprobs_mode
@@ -1013,3 +1091,44 @@ def forward_cuda(
             cu_batch_token_offset=share_inputs["cu_batch_token_offset"],
         )
         return next_tokens, sampler_output
+
+    def forward_xpu(
+        self,
+        logits: paddle.Tensor,
+        sampling_metadata: SamplingMetadata,
+        max_model_len: int,
+        share_inputs: List[paddle.Tensor],
+    ) -> paddle.Tensor:
+
+        logits = apply_speculative_penalty_multi_scores(
+            sampling_metadata.pre_token_ids,
+            logits,
+            sampling_metadata.repetition_penalties,
+            sampling_metadata.frequency_penalties,
+            sampling_metadata.presence_penalties,
+            sampling_metadata.temperature,
+            sampling_metadata.bad_words_token_ids,
+            sampling_metadata.step_idx,
+            sampling_metadata.min_dec_lens,
+            sampling_metadata.eos_token_ids,
+            share_inputs["seq_lens_this_time"],
+            share_inputs["output_padding_offset"],
+            share_inputs["output_cum_offsets"],
+            max_model_len,
+        )
+        probs = F.softmax(logits)
+
+        _, next_tokens = top_k_top_p_sampling(
+            probs, sampling_metadata.top_p, sampling_metadata.top_k, sampling_metadata.top_k_list
+        )
+        # TODO(chenhuan09): add support for logprobs
+        token_ids = None
+        logprobs_tensors = None
+
+        sampler_output = SamplerOutput(
+            sampled_token_ids=token_ids,
+            logprobs_tensors=logprobs_tensors,
+            token_num_per_batch=None,
+            cu_batch_token_offset=None,
+        )
+        return next_tokens, sampler_output