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Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
https://coremltools.readme.io
BSD 3-Clause "New" or "Revised" License
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Failed to build the model execution plan using a model architecture file #2325

Open Skyline-23 opened 2 weeks ago

Skyline-23 commented 2 weeks ago

๐ŸžDescribing the bug

Hello. I'm trying to convert PyTorch model to Stateful CoreML Model

I wrote this code referred to WWDC 2024 session Mistral-7B model The CoreML file is appear after run, but "Failed to build the model execution plan using a model architecture file" error appears when CoreML Class init

Stack Trace

/opt/homebrew/lib/python3.11/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
  warnings.warn(
/opt/homebrew/lib/python3.11/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Tensor-likes are not close!

Mismatched elements: 12 / 90000 (0.0%)
Greatest absolute difference: 1.6361474990844727e-05 at index (0, 11, 1251) (up to 1e-05 allowed)
Greatest relative difference: 0.000991315116234805 at index (0, 12, 1660) (up to 1e-05 allowed)
  _check_trace(
Torch var valueCache is added again.
Torch var keyCache is added again.
Converting PyTorch Frontend ==> MIL Ops: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1516/1516 [00:00<00:00, 2464.91 ops/s]
Running MIL frontend_pytorch pipeline: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 5/5 [00:00<00:00, 27.54 passes/s]
Running MIL default pipeline:  60%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–                                  | 52/86 [00:02<00:01, 27.17 passes/s]/opt/homebrew/lib/python3.11/site-packages/coremltools/converters/mil/mil/ops/defs/iOS15/elementwise_unary.py:894: RuntimeWarning: overflow encountered in cast
  return input_var.val.astype(dtype=string_to_nptype(dtype_val))
Running MIL default pipeline: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 86/86 [00:06<00:00, 13.08 passes/s]
Running MIL backend_mlprogram pipeline: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 12/12 [00:00<00:00, 64.39 passes/s]
/opt/homebrew/lib/python3.11/site-packages/coremltools/models/model.py:441: RuntimeWarning: You will not be able to run predict() on this Core ML model. Underlying exception message was: {
    NSLocalizedDescription = "Failed to build the model execution plan using a model architecture file '/private/var/folders/pz/rmstwmls5ls_0hrn5_jj01kh0000gn/T/tmplybl8sp_.mlmodelc/model.mil' with error code: 14.";
}
  _warnings.warn(
Model successfully converted and saved as: zenz_v1_cached.mlpackage

To Reproduce

import torch
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel, GPT2Attention, GPT2_ATTENTION_CLASSES
from transformers import AutoTokenizer
import coremltools as ct
from typing import Optional, Tuple
import numpy as np
from transformers.cache_utils import Cache
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

class SliceUpdateKeyValueCache(Cache):
    def __init__(self, shape: Tuple[int, ...], device="cpu", dtype=torch.float32) -> None:
        """KV cache of shape (#layers, batch_size, #kv_heads, context_size, head_dim)."""
        super().__init__()
        self.past_seen_tokens: int = 0
        self.k_cache: torch.Tensor = torch.zeros(shape, dtype=dtype, device=device)
        self.v_cache: torch.Tensor = torch.zeros(shape, dtype=dtype, device=device)

    def update(self, k_state: torch.Tensor, v_state: torch.Tensor, layer_idx: int, slice_indices: torch.LongTensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Update key/value cache tensors for slice [slice_indices[0], slice_indices[1])."""
        if len(slice_indices) != 2:
            raise ValueError(f"Expect tuple of integers [start, end), got {slice_indices=}.")
        begin, end = slice_indices
        self.k_cache[layer_idx, :, : k_state.shape[1], begin:end, :] = k_state
        self.v_cache[layer_idx, :, : v_state.shape[1], begin:end, :] = v_state
        return self.k_cache[layer_idx, :, :, :end, :], self.v_cache[layer_idx, :, :, :end, :]

    def get_seq_length(self, _: int = 0) -> int:
        """Get the sequence length of the cache."""
        return self.past_seen_tokens

    def to_past_key_values(self):
        """Convert the internal cache to a format expected by GPT2."""
        return [(self.k_cache[layer], self.v_cache[layer]) for layer in range(self.k_cache.size(0))]

class SliceUpdateGPT2Attention(GPT2Attention):
    def __init__(self, config, layer_idx: Optional[int] = None):
        super().__init__(config=config, layer_idx=layer_idx)

    @torch.no_grad()
    def forward(self, hidden_states: torch.Tensor, 
                layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
                attention_mask: Optional[torch.FloatTensor] = None, 
                head_mask: Optional[torch.FloatTensor] = None,
                use_cache: bool = False) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
        # Compute query, key, and value tensors
        query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
        query = self._split_heads(query, self.num_heads, self.head_dim)
        key = self._split_heads(key, self.num_heads, self.head_dim)
        value = self._split_heads(value, self.num_heads, self.head_dim)

        # Handle past key/value tensors using tensor-based condition
        if layer_past is not None:
            past_key, past_value = layer_past
            if past_key.size(-2) > 0:
                key = torch.cat([past_key, key], dim=-2)
                value = torch.cat([past_value, value], dim=-2)

        # Optimize attention mask handling
        if attention_mask is not None:
            attention_mask = attention_mask[:, :, :, -key.size(-2):]

        # Calculate attention output
        attn_output, _ = self._attn(query, key, value, attention_mask, head_mask)
        attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
        attn_output = self.c_proj(attn_output)

        # Return the updated cache if use_cache is True
        present = (key, value) if use_cache else None
        return attn_output, present

# Load the model and tokenizer
model_name = "Miwa-Keita/zenz-v1-checkpoints"
GPT2_ATTENTION_CLASSES["sdpa"] = SliceUpdateGPT2Attention
model = GPT2LMHeadModel.from_pretrained(model_name).eval()
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare input data
text = "Example sentence"
inputs = tokenizer(text, return_tensors="pt")

# Model tracing
class StatefulZenz(torch.nn.Module):
    def __init__(self, model, max_context_size: int = 256, batch_size: int = 1):
        super(StatefulZenz, self).__init__()
        self.model = model
        config = self.model.config
        self.kv_cache_shape: Tuple[int, ...] = (
            config.num_hidden_layers,
            batch_size,
            config.n_head,
            max_context_size,
            config.hidden_size // config.num_attention_heads,
        )
        self.kv_cache = SliceUpdateKeyValueCache(shape=self.kv_cache_shape)
        self.register_buffer("keyCache", self.kv_cache.k_cache)
        self.register_buffer("valueCache", self.kv_cache.v_cache)

    @torch.no_grad()
    def forward(self, input_ids, attention_mask):
        self.kv_cache.past_seen_tokens = attention_mask.shape[-1] - input_ids.shape[-1]
        past_key_values = self.kv_cache.to_past_key_values()

        # Reintroduce the attention mask extension logic
        attention_mask = self._extend_attention_mask(attention_mask, past_key_values)

        outputs = self.model(input_ids, attention_mask=attention_mask, past_key_values=past_key_values, use_cache=True)
        return outputs.logits

    def _extend_attention_mask(self, attention_mask, past_key_values):
        """Adjust the attention mask to match the size of the key/value cache."""
        if past_key_values is not None:
            past_length = past_key_values[0][0].size(-2)
            new_length = past_length + attention_mask.size(-1)
            extended_attention_mask = torch.ones(
                (attention_mask.size(0), 1, 1, new_length), dtype=attention_mask.dtype, device=attention_mask.device
            )
            extended_attention_mask[:, :, :, -attention_mask.size(-1):] = attention_mask
        else:
            extended_attention_mask = attention_mask
        return extended_attention_mask

# Create the traced model
stateful_zenz = StatefulZenz(model).eval()
traced_model = torch.jit.trace(stateful_zenz, (inputs['input_ids'], inputs['attention_mask']))

# Convert the model to CoreML
mlmodel = ct.convert(
    traced_model,
    inputs=[
        ct.TensorType(name="input_ids", shape=(1, ct.RangeDim(1, 256))),  # ไธŠ้™ใ‚’256ใซ่จญๅฎš
        ct.TensorType(name="attention_mask", shape=(1, ct.RangeDim(1, 256)))  # ไธŠ้™ใ‚’256ใซ่จญๅฎš
    ],
    outputs=[
        ct.TensorType(dtype=np.float32, name="output")
    ],
    states=[
        ct.StateType(
            wrapped_type=ct.TensorType(
                shape=stateful_zenz.kv_cache_shape
            ),
            name="keyCache",
        ),
        ct.StateType(
            wrapped_type=ct.TensorType(
                shape=stateful_zenz.kv_cache_shape
            ),
            name="valueCache",
        ),
    ],
    minimum_deployment_target=ct.target.iOS18
)

# Save the converted model
mlmodel.save("zenz_v1_cached.mlpackage")
print("Model successfully converted and saved as: zenz_v1_cached.mlpackage")

System environment (please complete the following information):

TobyRoseman commented 2 weeks ago

@Skyline-23 that is a lot of code. Can you give us a more minimal example?

Skyline-23 commented 1 week ago

@TobyRoseman All of code is required to run stateful model based on GPT-2. Sorry ๐Ÿ˜ข