0c94f60093
* Initial Intel ARC support with IPEX * Fix infer * Fix train model * Cleanup * Cleanup * Update README * Make pylint happy * Move dataloader fix to hijacks * Fix torch.linalg.solve * Fix SDP * Add has_xpu to config.py * Revert return_xpu fix
129 lines
6.1 KiB
Python
129 lines
6.1 KiB
Python
import torch
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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# pylint: disable=protected-access, missing-function-docstring, line-too-long
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original_torch_bmm = torch.bmm
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def torch_bmm(input, mat2, *, out=None):
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if input.dtype != mat2.dtype:
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mat2 = mat2.to(input.dtype)
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#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2]
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block_multiply = 2.4 if input.dtype == torch.float32 else 1.2
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block_size = (batch_size_attention * input_tokens * mat2_shape) / 1024 * block_multiply #MB
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split_slice_size = batch_size_attention
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if block_size >= 4000:
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do_split = True
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#Find something divisible with the input_tokens
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while ((split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply) > 4000:
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split_slice_size = split_slice_size // 2
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if split_slice_size <= 1:
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split_slice_size = 1
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break
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else:
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do_split = False
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split_block_size = (split_slice_size * input_tokens * mat2_shape) / 1024 * block_multiply #MB
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split_2_slice_size = input_tokens
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if split_block_size >= 4000:
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do_split_2 = True
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#Find something divisible with the input_tokens
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while ((split_slice_size * split_2_slice_size * mat2_shape) / 1024 * block_multiply) > 4000:
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split_2_slice_size = split_2_slice_size // 2
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if split_2_slice_size <= 1:
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split_2_slice_size = 1
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break
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else:
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do_split_2 = False
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if do_split:
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hidden_states = torch.zeros(input.shape[0], input.shape[1], mat2.shape[2], device=input.device, dtype=input.dtype)
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for i in range(batch_size_attention // split_slice_size):
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start_idx = i * split_slice_size
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end_idx = (i + 1) * split_slice_size
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if do_split_2:
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for i2 in range(input_tokens // split_2_slice_size): # pylint: disable=invalid-name
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start_idx_2 = i2 * split_2_slice_size
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end_idx_2 = (i2 + 1) * split_2_slice_size
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hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm(
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input[start_idx:end_idx, start_idx_2:end_idx_2],
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mat2[start_idx:end_idx, start_idx_2:end_idx_2],
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out=out
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)
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else:
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hidden_states[start_idx:end_idx] = original_torch_bmm(
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input[start_idx:end_idx],
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mat2[start_idx:end_idx],
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out=out
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)
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else:
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return original_torch_bmm(input, mat2, out=out)
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return hidden_states
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original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
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def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
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#ARC GPUs can't allocate more than 4GB to a single block, Slice it:
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shape_one, batch_size_attention, query_tokens, shape_four = query.shape
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block_multiply = 2.4 if query.dtype == torch.float32 else 1.2
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block_size = (shape_one * batch_size_attention * query_tokens * shape_four) / 1024 * block_multiply #MB
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split_slice_size = batch_size_attention
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if block_size >= 4000:
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do_split = True
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#Find something divisible with the shape_one
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while ((shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply) > 4000:
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split_slice_size = split_slice_size // 2
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if split_slice_size <= 1:
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split_slice_size = 1
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break
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else:
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do_split = False
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split_block_size = (shape_one * split_slice_size * query_tokens * shape_four) / 1024 * block_multiply #MB
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split_2_slice_size = query_tokens
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if split_block_size >= 4000:
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do_split_2 = True
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#Find something divisible with the batch_size_attention
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while ((shape_one * split_slice_size * split_2_slice_size * shape_four) / 1024 * block_multiply) > 4000:
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split_2_slice_size = split_2_slice_size // 2
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if split_2_slice_size <= 1:
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split_2_slice_size = 1
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break
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else:
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do_split_2 = False
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if do_split:
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hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype)
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for i in range(batch_size_attention // split_slice_size):
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start_idx = i * split_slice_size
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end_idx = (i + 1) * split_slice_size
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if do_split_2:
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for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
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start_idx_2 = i2 * split_2_slice_size
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end_idx_2 = (i2 + 1) * split_2_slice_size
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hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention(
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query[:, start_idx:end_idx, start_idx_2:end_idx_2],
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key[:, start_idx:end_idx, start_idx_2:end_idx_2],
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value[:, start_idx:end_idx, start_idx_2:end_idx_2],
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attn_mask=attn_mask[:, start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask,
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dropout_p=dropout_p, is_causal=is_causal
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)
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else:
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hidden_states[:, start_idx:end_idx] = original_scaled_dot_product_attention(
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query[:, start_idx:end_idx],
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key[:, start_idx:end_idx],
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value[:, start_idx:end_idx],
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attn_mask=attn_mask[:, start_idx:end_idx] if attn_mask is not None else attn_mask,
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dropout_p=dropout_p, is_causal=is_causal
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)
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else:
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return original_scaled_dot_product_attention(
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query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal
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)
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return hidden_states
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def attention_init():
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#ARC GPUs can't allocate more than 4GB to a single block:
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torch.bmm = torch_bmm
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torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
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