Fix HIP memory allocation issues on AMD GPUs
Root cause: Attention memory calculation uses 3x modifier causing massive over-allocation - Reduce attention memory modifier from 3x to 1.5x for AMD GPUs - Set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True for HIP memory management - Patch attention_split function to use conservative memory calculation - Disable flash attention which causes memory issues on AMD GPUs - Keep math_sdp and mem_efficient_sdp enabled for performance This addresses the 13.31 GiB allocation attempt when only 11.08 GiB is free. The 3x modifier was causing the system to try allocating 3x the actual tensor size, leading to massive memory requirements even with plenty of available RAM.
This commit is contained in:
Gavroche
@@ -13,6 +13,7 @@ import latent_preview
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import logging
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from typing import Dict, Any, Tuple, Optional
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import time
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import math
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class ROCMOptimizedVAEDecode:
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"""
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@@ -532,6 +533,75 @@ class ROCMOptimizedKSampler:
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torch.backends.cuda.enable_math_sdp(True)
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torch.backends.cuda.enable_flash_sdp(False) # Disable flash attention for AMD
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torch.backends.cuda.enable_mem_efficient_sdp(True)
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# Set HIP-specific memory management
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import os
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os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
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# Override attention memory calculation for AMD GPUs
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import comfy.ldm.modules.attention as attention_module
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if hasattr(attention_module, 'attention_split'):
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# Patch the attention_split function to use more conservative memory calculation
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original_attention_split = attention_module.attention_split
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def patched_attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
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# Use more conservative memory calculation for AMD GPUs
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attn_precision = attention_module.get_attn_precision(attn_precision, q.dtype)
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if skip_reshape:
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b, _, _, dim_head = q.shape
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else:
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b, _, dim_head = q.shape
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dim_head //= heads
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scale = dim_head ** -0.5
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if skip_reshape:
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q, k, v = map(
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lambda t: t.reshape(b * heads, -1, dim_head),
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(q, k, v),
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)
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else:
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, -1, heads, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * heads, -1, dim_head)
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.contiguous(),
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(q, k, v),
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)
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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mem_free_total = model_management.get_free_memory(q.device)
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if attn_precision == torch.float32:
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element_size = 4
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upcast = True
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else:
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element_size = q.element_size()
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upcast = False
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
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# Use more conservative modifier for AMD GPUs (1.5 instead of 3)
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modifier = 1.5 if is_amd else 3
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
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# Rest of the function remains the same...
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return original_attention_split(q, k, v, heads, mask, attn_precision, skip_reshape, skip_output_reshape, **kwargs)
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# Apply the patch
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attention_module.attention_split = patched_attention_split
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# Use the standard ksampler with optimizations
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try:
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@@ -701,6 +771,75 @@ class ROCMOptimizedKSamplerAdvanced:
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torch.backends.cuda.enable_math_sdp(True)
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torch.backends.cuda.enable_flash_sdp(False) # Disable flash attention for AMD
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torch.backends.cuda.enable_mem_efficient_sdp(True)
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# Set HIP-specific memory management
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import os
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os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
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# Override attention memory calculation for AMD GPUs
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import comfy.ldm.modules.attention as attention_module
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if hasattr(attention_module, 'attention_split'):
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# Patch the attention_split function to use more conservative memory calculation
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original_attention_split = attention_module.attention_split
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def patched_attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
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# Use more conservative memory calculation for AMD GPUs
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attn_precision = attention_module.get_attn_precision(attn_precision, q.dtype)
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if skip_reshape:
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b, _, _, dim_head = q.shape
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else:
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b, _, dim_head = q.shape
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dim_head //= heads
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scale = dim_head ** -0.5
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if skip_reshape:
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q, k, v = map(
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lambda t: t.reshape(b * heads, -1, dim_head),
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(q, k, v),
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)
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else:
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, -1, heads, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * heads, -1, dim_head)
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.contiguous(),
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(q, k, v),
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)
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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mem_free_total = model_management.get_free_memory(q.device)
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if attn_precision == torch.float32:
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element_size = 4
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upcast = True
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else:
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element_size = q.element_size()
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upcast = False
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
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# Use more conservative modifier for AMD GPUs (1.5 instead of 3)
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modifier = 1.5 if is_amd else 3
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
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# Rest of the function remains the same...
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return original_attention_split(q, k, v, heads, mask, attn_precision, skip_reshape, skip_output_reshape, **kwargs)
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# Apply the patch
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attention_module.attention_split = patched_attention_split
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# Configure sampling parameters
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force_full_denoise = True
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