add use_deepspeed
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@ -352,7 +352,7 @@ class UnifiedVoice(nn.Module):
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for module in embeddings:
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module.weight.data.normal_(mean=0.0, std=.02)
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def post_init_gpt2_config(self, kv_cache=False):
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def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False):
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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
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n_positions=seq_length,
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@ -363,6 +363,17 @@ class UnifiedVoice(nn.Module):
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gradient_checkpointing=False,
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use_cache=True)
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self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head, kv_cache=kv_cache)
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#print(f'use_deepspeed autoregressive_debug {use_deepspeed}')
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if use_deepspeed and torch.cuda.is_available():
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import deepspeed
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self.ds_engine = deepspeed.init_inference(model=self.inference_model,
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mp_size=1,
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replace_with_kernel_inject=True,
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dtype=torch.float32)
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self.inference_model = self.ds_engine.module.eval()
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else:
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self.inference_model = self.inference_model.eval()
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self.gpt.wte = self.mel_embedding
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def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
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