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[XPU] modify speculate_verify (#5522)
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@@ -71,7 +71,7 @@ def padding_sampling_params(top_p, top_k, infer_seed, seq_lens_this_time, seq_le
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paddle.ones_like(seq_lens_this_time),
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)
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batch_start = (paddle.cumsum(token_lens, axis=0) - token_lens.astype("int64")).reshape(-1) # [B]
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batch_start = (paddle.cumsum(token_lens, axis=0, dtype="int64") - token_lens.astype("int64")).reshape([-1]) # [B]
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token_batch_ids = paddle.repeat_interleave(
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paddle.arange(token_lens.shape[0], dtype="int64"),
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token_lens,
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@@ -79,7 +79,7 @@ def padding_sampling_params(top_p, top_k, infer_seed, seq_lens_this_time, seq_le
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token_pos = paddle.arange(topp_seed.shape[0], dtype="int64")
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local_pos = token_pos - paddle.gather(batch_start, token_batch_ids)
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is_decoder = paddle.gather(seq_lens_encoder[:real_bsz] == 0, token_batch_ids).reshape(-1)
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is_decoder = paddle.gather(seq_lens_encoder[:real_bsz] == 0, token_batch_ids).reshape([-1])
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offsets = paddle.where(
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is_decoder,
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@@ -879,6 +879,15 @@ class SpeculativeSampler(nn.Layer):
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probs = F.softmax(logits)
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top_p, top_k, topp_seed = padding_sampling_params(
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sampling_metadata.top_p,
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sampling_metadata.top_k,
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sampling_metadata.seed,
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share_inputs["seq_lens_this_time"],
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paddle.reshape(share_inputs["seq_lens_encoder"], shape=[-1]),
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)
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_, sampled_token_ids = top_k_top_p_sampling(probs, top_p=top_p, top_k=top_k, topp_seed=topp_seed)
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verify_scores, verify_tokens, actual_candidate_len = top_p_candidates(
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probs,
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sampling_metadata.top_p,
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@@ -888,6 +897,7 @@ class SpeculativeSampler(nn.Layer):
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)
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speculate_verify(
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sampled_token_ids,
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share_inputs["accept_tokens"],
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share_inputs["accept_num"],
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share_inputs["step_idx"],
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