[ET-VK] Allocate memory for weight and activation tensors lazily by SS-JIA · Pull Request #13474 · pytorch/executorch

SS-JIA · 2025-08-18T15:16:33Z

Stack from ghstack (oldest at bottom):

Summary:

Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph
Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking

Motivation

Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model.

Full Context

During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points:

source data obtained from loading the model
staging buffer
GPU texture/buffer

The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once.

Current Order of operations

Legend:

W represents total weight nbytes
w represents weight nbytes for one tensor
A represents total activations nbytes
M represents approximation of total memory footprint

First, model file is loaded

Then, when building compute graph, for each weight tensor:

Weight data is loaded from NamedDataMap (M = W)
GPU texture/buffer for weight is initialized + memory allocated (M = 2W)
After building the graph, graph->prepare() is called which currently allocates memory for the activation tensors as well (M = 2W + A)

Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually:

Staging buffer initialized (M = 2W + A + w)
Copy CPU weight data to staging + CPU Weight data is freed (M = 2W + A)
Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (M = 2W + A - w)

The peak usage in mainline will be M = 2W + A + w

Revised order of operations

This change revises the order of operations:

Weight data is loaded from NamedDataMap (M = W)
GPU texture/buffer for weight is initialized, but memory is not allocated (M = W)

Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually:

Staging buffer initialized (M = W + w)
Memory allocated for GPU texture/buffer (M = W + 2w)
Copy CPU weight data to staging + CPU Weight data is freed (M = W + w)
Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (M = W)

Then, after all prepacking operations complete, only then is Activation memory allocated (M = W + A)

Under this scheme, peak memory is reduced to M = W + A (or alternatively M = W + 2w if 2w > A) which is (or at least very close to) the theoretical minimum.

Test Plan:

Logging Memory Usage

Using

uint64_t getVmRssInKB() {
  std::ifstream statusFile("/proc/self/status");
  std::string l, num;
  while (std::getline(statusFile, l)) {
    if (l.substr(0, 5) == "VmRSS") {
      size_t pos = l.find_first_of("0123456789");
      num = l.substr(pos);
      break;
    }
  }
  uint64_t vmRssInKB = std::stoi(num);
  return vmRssInKB;
}

uint64_t getVmaStatsInKB() {
  auto stats =
      vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics();
  uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10;
  return vmaBlockInKB;
}

to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes.

With changes: P1908051860 (Meta only)

Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA)
Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA)
Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA)
Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB
Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA)

Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA)
Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA)

WIthout changes: P1908054759 (Meta only)

Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA)
Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA)
Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA)
Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB
Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA)

Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA)
Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA)

It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph.

Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model.

Differential Revision: D80460033

Summary: * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. Test Plan: ## Logging Memory Usage Using ``` uint64_t getVmRssInKB() { std::ifstream statusFile("/proc/self/status"); std::string l, num; while (std::getline(statusFile, l)) { if (l.substr(0, 5) == "VmRSS") { size_t pos = l.find_first_of("0123456789"); num = l.substr(pos); break; } } uint64_t vmRssInKB = std::stoi(num); return vmRssInKB; } uint64_t getVmaStatsInKB() { auto stats = vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics(); uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10; return vmaBlockInKB; } ``` to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes. With changes: P1908051860 (Meta only) ``` Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA) Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA) Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA) ``` WIthout changes: P1908054759 (Meta only) ``` Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA) Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA) Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA) ``` It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph. Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model. [ghstack-poisoned]

pytorch-bot · 2025-08-18T15:16:36Z

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👉 Rebase onto the `viable/strict` branch to avoid these failures

pull / test-binary-size-linux-gcc / linux-job (gh) (trunk failure)
/pytorch/executorch/kernels/portable/cpu/op_stack.cpp:129:26: error: comparison of integer expressions of different signedness: ‘size_t’ {aka ‘long unsigned int’} and ‘ssize_t’ {aka ‘long int’} [-Werror=sign-compare]

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Summary: * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. Test Plan: ## Logging Memory Usage Using ``` uint64_t getVmRssInKB() { std::ifstream statusFile("/proc/self/status"); std::string l, num; while (std::getline(statusFile, l)) { if (l.substr(0, 5) == "VmRSS") { size_t pos = l.find_first_of("0123456789"); num = l.substr(pos); break; } } uint64_t vmRssInKB = std::stoi(num); return vmRssInKB; } uint64_t getVmaStatsInKB() { auto stats = vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics(); uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10; return vmaBlockInKB; } ``` to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes. With changes: P1908051860 (Meta only) ``` Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA) Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA) Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA) ``` WIthout changes: P1908054759 (Meta only) ``` Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA) Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA) Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA) ``` It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph. Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model. ghstack-source-id: b2d943d Pull Request resolved: #13474

SS-JIA · 2025-08-18T15:25:48Z

@SS-JIA has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

…lazily" Summary: * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. Test Plan: ## Logging Memory Usage Using ``` uint64_t getVmRssInKB() { std::ifstream statusFile("/proc/self/status"); std::string l, num; while (std::getline(statusFile, l)) { if (l.substr(0, 5) == "VmRSS") { size_t pos = l.find_first_of("0123456789"); num = l.substr(pos); break; } } uint64_t vmRssInKB = std::stoi(num); return vmRssInKB; } uint64_t getVmaStatsInKB() { auto stats = vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics(); uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10; return vmaBlockInKB; } ``` to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes. With changes: P1908051860 (Meta only) ``` Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA) Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA) Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA) ``` WIthout changes: P1908054759 (Meta only) ``` Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA) Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA) Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA) ``` It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph. Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model. Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033) [ghstack-poisoned]

Pull Request resolved: #13474 * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033/) ghstack-source-id: 303779654

facebook-github-bot · 2025-08-18T17:28:59Z

This pull request was exported from Phabricator. Differential Revision: D80460033

…lazily" Summary: * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. Test Plan: ## Logging Memory Usage Using ``` uint64_t getVmRssInKB() { std::ifstream statusFile("/proc/self/status"); std::string l, num; while (std::getline(statusFile, l)) { if (l.substr(0, 5) == "VmRSS") { size_t pos = l.find_first_of("0123456789"); num = l.substr(pos); break; } } uint64_t vmRssInKB = std::stoi(num); return vmRssInKB; } uint64_t getVmaStatsInKB() { auto stats = vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics(); uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10; return vmaBlockInKB; } ``` to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes. With changes: P1908051860 (Meta only) ``` Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA) Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA) Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA) ``` WIthout changes: P1908054759 (Meta only) ``` Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA) Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA) Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA) ``` It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph. Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model. Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033) [ghstack-poisoned]

Pull Request resolved: #13474 * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. ghstack-source-id: 303830115 Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033/)

facebook-github-bot · 2025-08-18T20:15:18Z

This pull request was exported from Phabricator. Differential Revision: D80460033

…lazily" Summary: * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. Test Plan: ## Logging Memory Usage Using ``` uint64_t getVmRssInKB() { std::ifstream statusFile("/proc/self/status"); std::string l, num; while (std::getline(statusFile, l)) { if (l.substr(0, 5) == "VmRSS") { size_t pos = l.find_first_of("0123456789"); num = l.substr(pos); break; } } uint64_t vmRssInKB = std::stoi(num); return vmRssInKB; } uint64_t getVmaStatsInKB() { auto stats = vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics(); uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10; return vmaBlockInKB; } ``` to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes. With changes: P1908051860 (Meta only) ``` Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA) Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA) Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA) ``` WIthout changes: P1908054759 (Meta only) ``` Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA) Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA) Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA) Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA) Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA) Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA) ``` It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph. Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model. Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033) [ghstack-poisoned]

Pull Request resolved: #13474 * Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph * Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking ## Motivation Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model. ## Full Context During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points: * source data obtained from loading the model * staging buffer * GPU texture/buffer The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once. ### Current Order of operations Legend: * `W` represents total weight nbytes * `w` represents weight nbytes for one tensor * `A` represents total activations nbytes * `M` represents approximation of total memory footprint First, model file is loaded Then, when building compute graph, for each weight tensor: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`) 3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = 2W + A + w`) 2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`) 3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`) The peak usage in mainline will be `M = 2W + A + w` ### Revised order of operations This change revises the order of operations: 1. Weight data is loaded from NamedDataMap (`M = W`) 2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`) Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually: 1. Staging buffer initialized (`M = W + w`) 2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`) 3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`) 4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`) **Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`) Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum. ghstack-source-id: 303862303 Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033/)

facebook-github-bot · 2025-08-18T21:53:49Z

This pull request was exported from Phabricator. Differential Revision: D80460033

Summary: It seems #13474 was not merged correctly via the cherry pick bot. This PR manually syncs internal and fbcode. [ghstack-poisoned]

Summary: It seems #13474 was not merged correctly via the cherry pick bot. This PR manually syncs internal and fbcode. ghstack-source-id: fe9c81a Pull Request resolved: #13512

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * __->__ #13512 Summary: It seems #13474 was not merged correctly via the cherry pick bot. This PR manually syncs internal and fbcode.

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * __->__ pytorch#13512 Summary: It seems pytorch#13474 was not merged correctly via the cherry pick bot. This PR manually syncs internal and fbcode.

This was referenced Aug 18, 2025

[ET-VK] Runtime support for NamedDataMap #13472

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[ET-VK][AOT] Serialize constant tensors via NamedDataMap #13473

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meta-cla Bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Aug 18, 2025

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[Vulkan] Reduce peak memory usage when loading models with ET-VK #13475

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[ET-VK][ez] Move execute node threshold calculation from prepare_pipelines() to prepare() #13478

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trviv added the release notes: vulkan Changes to the Vulkan backend delegate label Aug 18, 2025

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facebook-github-bot merged commit ac46761 into gh/SS-JIA/292/base Aug 19, 2025
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facebook-github-bot deleted the gh/SS-JIA/292/head branch August 19, 2025 02:24

facebook-github-bot temporarily deployed to cherry-pick-bot August 19, 2025 02:24 — with GitHub Actions Inactive

This was referenced Aug 19, 2025

[ET-VK] Allocate memory for weight and activation tensors lazily #13500

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[ET-VK] Allocate memory for weight and activation tensors lazily #13501

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SS-JIA added a commit that referenced this pull request Aug 19, 2025

[ET-VK][ez] Fix erroneous manual merge

d069040

Summary: It seems #13474 was not merged correctly via the cherry pick bot. This PR manually syncs internal and fbcode. [ghstack-poisoned]

SS-JIA mentioned this pull request Aug 19, 2025

[ET-VK][ez] Fix erroneous cherry-pick bot merge #13512

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SS-JIA added a commit that referenced this pull request Aug 19, 2025

[ET-VK][ez] Fix erroneous manual merge

d7ef0f0

Summary: It seems #13474 was not merged correctly via the cherry pick bot. This PR manually syncs internal and fbcode. ghstack-source-id: fe9c81a Pull Request resolved: #13512

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[ET-VK] Allocate memory for weight and activation tensors lazily#13474

[ET-VK] Allocate memory for weight and activation tensors lazily#13474
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