Add support for QAT + LoRA

**Summary:**

This commit adds a recipe that combines QAT + LoRA, with the main goal of improving final quantized accuracy after training while reducing the memory required for fine-tuning. The new recipe `qat_lora_finetune_distributed` mirrors the existing `lora_finetune_distributed` recipe, which performs only LoRA, and is analogous to the existing `qat_distributed` recipe, which performs only QAT.

Helpful code review commands:
```
diff --color recipes/lora_finetune_distributed.py recipes/qat_lora_finetune_distributed.py
diff --color recipes/configs/llama3/8B_lora.yaml recipes/configs/llama3/8B_qat_lora.yaml
diff --color recipes/configs/llama3_1/8B_lora.yaml recipes/configs/llama3_1/8B_qat_lora.yaml
diff --color recipes/configs/llama3_2/1B_lora.yaml recipes/configs/llama3_2/1B_qat_lora.yaml
diff --color recipes/configs/llama3_2/3B_lora.yaml recipes/configs/llama3_2/3B_qat_lora.yaml
```

For more context on QAT, please visit #980 and https://pytorch.org/blog/quantization-aware-training/.

**Test Plan**

Unit tests:
```
pytest -m integration_test tests/recipes/test_qat_lora_finetune_distributed.py
```

Manual tests:
```
export CUDA_VISIBLE_DEVICES=4,5,6,7
export NCCL_SHM_DISABLE=0
LOG_DIR=/home/andrewor/local/logs/tune/qat_lora

tune run --nnodes 1 --nproc_per_node 4 qat_lora_finetune_distributed --config llama3/8B_qat_lora \
    batch_size=4 \
    quantizer.groupsize=32 \
    checkpointer.output_dir="$LOG_DIR" \
    metric_logger.output_dir="${LOG_DIR}/metrics"

tune run quantize --config quantization \
    model._component_=torchtune.models.llama3.llama3_8b \
    checkpointer._component_=torchtune.training.FullModelMetaCheckpointer \
    checkpointer.checkpoint_dir="$LOG_DIR" \
    checkpointer.output_dir="$LOG_DIR" \
    checkpointer.checkpoint_files=["meta_model_0.pt"] \
    checkpointer.model_type=LLAMA3 \
    quantizer._component_=torchtune.training.quantization.Int8DynActInt4WeightQuantizer \
    quantizer.groupsize=32

tune run eleuther_eval --config eleuther_evaluation \
    batch_size=1 \
    model._component_=torchtune.models.llama3.llama3_8b \
    checkpointer._component_=torchtune.training.FullModelTorchTuneCheckpointer \
    checkpointer.checkpoint_dir="$LOG_DIR" \
    checkpointer.output_dir="$LOG_DIR" \
    checkpointer.checkpoint_files=["meta_model_0.pt-8da4w"] \
    checkpointer.model_type=LLAMA3 \
    tokenizer._component_=torchtune.models.llama3.llama3_tokenizer \
    tokenizer.path=/tmp/Meta-Llama-3-8B-Instruct/original/tokenizer.model \
    tasks=[wikitext] \
    quantizer._component_=torchtune.training.quantization.Int8DynActInt4WeightQuantizer \
    quantizer.groupsize=32
```

Results:
```

| Tasks  |Version|Filter|n-shot|    Metric     |   | Value |   |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext|      2|none  |None  |bits_per_byte  |↓  | 0.6284|±  |   N/A|
|        |       |none  |None  |byte_perplexity|↓  | 1.5458|±  |   N/A|
|        |       |none  |None  |word_perplexity|↓  |10.2694|±  |   N/A|

| Tasks  |Version|Filter|n-shot|    Metric     |   | Value |   |Stderr|
|--------|------:|------|------|---------------|---|------:|---|------|
|wikitext|      2|none  |None  |bits_per_byte  |↓  | 0.6245|±  |   N/A|
|        |       |none  |None  |byte_perplexity|↓  | 1.5416|±  |   N/A|
|        |       |none  |None  |word_perplexity|↓  |10.1208|±  |   N/A|
```

recipes/configs/llama3/8B_qat_lora.yaml

@@ -0,0 +1,113 @@
+# Config for multi-device QAT + LoRA finetuning in qat_lora_finetune_distributed.py
+# using a Llama3 8B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3-8B-Instruct --output-dir /tmp/Meta-Llama-3-8B-Instruct --hf-token <HF_TOKEN>
+#
+# To launch on 2 devices, run the following command from root:
+#   tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3/8B_qat_lora
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3/8B_qat_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Meta-Llama-3-8B-Instruct/original/tokenizer.model
+  max_seq_len: null
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3.lora_llama3_8b
+  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
+  apply_lora_to_mlp: True
+  apply_lora_to_output: False
+  lora_rank: 8  # higher increases accuracy and memory
+  lora_alpha: 16  # usually alpha=2*rank
+  lora_dropout: 0.0
+
+checkpointer:
+  _component_: torchtune.training.FullModelMetaCheckpointer
+  checkpoint_dir: /tmp/Meta-Llama-3-8B-Instruct/original/
+  checkpoint_files: [
+    consolidated.00.pth
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Meta-Llama-3-8B-Instruct/
+  model_type: LLAMA3
+resume_from_checkpoint: False
+save_adapter_weights_only: False
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+  packed: False  # True increases speed
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 8  # Use to increase virtual batch size
+compile: False  # pytorch compile, set to true for better perf/memory
+
+# Logging
+output_dir: /tmp/qat_lora_finetune_output
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: False  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1
+
+# QAT arguments
+quantizer:
+  _component_: torchtune.training.quantization.Int8DynActInt4WeightQATQuantizer
+  groupsize: 256

recipes/configs/llama3_1/8B_qat_lora.yaml

@@ -0,0 +1,116 @@
+# Config for multi-device QAT + LoRA finetuning in qat_lora_finetune_distributed.py
+# using a Llama3.1 8B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Meta-Llama-3.1-8B-Instruct --output-dir /tmp/Meta-Llama-3.1-8B-Instruct --ignore-patterns "original/consolidated.00.pth"
+#
+# To launch on 2 devices, run the following command from root:
+#   tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3_1/8B_qat_lora
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3_1/8B_qat_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Meta-Llama-3.1-8B-Instruct/original/tokenizer.model
+  max_seq_len: null
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_1.lora_llama3_1_8b
+  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
+  apply_lora_to_mlp: True
+  apply_lora_to_output: False
+  lora_rank: 8  # higher increases accuracy and memory
+  lora_alpha: 16  # usually alpha=2*rank
+  lora_dropout: 0.0
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Meta-Llama-3.1-8B-Instruct/
+  checkpoint_files: [
+    model-00001-of-00004.safetensors,
+    model-00002-of-00004.safetensors,
+    model-00003-of-00004.safetensors,
+    model-00004-of-00004.safetensors
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Meta-Llama-3.1-8B-Instruct/
+  model_type: LLAMA3
+resume_from_checkpoint: False
+save_adapter_weights_only: False
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+  packed: False  # True increases speed
+seed: null
+shuffle: True
+batch_size: 2
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 8  # Use to increase virtual batch size
+compile: False  # pytorch compile, set to true for better perf/memory
+
+# Logging
+output_dir: /tmp/qat_lora_finetune_output
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: False  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1
+
+# QAT arguments
+quantizer:
+  _component_: torchtune.training.quantization.Int8DynActInt4WeightQATQuantizer
+  groupsize: 256

recipes/configs/llama3_2/1B_qat_lora.yaml

@@ -0,0 +1,112 @@
+# Config for multi-device QAT + LoRA finetuning in qat_lora_finetune_distributed.py
+# using a Llama3.2 1B Instruct model
+#
+# This config assumes that you've run the following command before launching
+# this run:
+#   tune download meta-llama/Llama-3.2-1B-Instruct --output-dir /tmp/Llama-3.2-1B-Instruct --ignore-patterns "original/consolidated.00.pth"
+#
+# To launch on 2 devices, run the following command from root:
+#   tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3_2/1B_qat_lora
+#
+# You can add specific overrides through the command line. For example
+# to override the checkpointer directory while launching training
+# you can run:
+#   tune run --nproc_per_node 2 qat_lora_finetune_distributed --config llama3_2/1B_qat_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
+
+# Tokenizer
+tokenizer:
+  _component_: torchtune.models.llama3.llama3_tokenizer
+  path: /tmp/Llama-3.2-1B-Instruct/original/tokenizer.model
+  max_seq_len: null
+
+# Model Arguments
+model:
+  _component_: torchtune.models.llama3_2.lora_llama3_2_1b
+  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
+  apply_lora_to_mlp: True
+  lora_rank: 64  # higher increases accuracy and memory
+  lora_alpha: 128  # usually alpha=2*rank
+  lora_dropout: 0.0
+
+checkpointer:
+  _component_: torchtune.training.FullModelHFCheckpointer
+  checkpoint_dir: /tmp/Llama-3.2-1B-Instruct/
+  checkpoint_files: [
+    model.safetensors
+  ]
+  recipe_checkpoint: null
+  output_dir: /tmp/Llama-3.2-1B-Instruct/
+  model_type: LLAMA3_2
+resume_from_checkpoint: False
+save_adapter_weights_only: False
+
+# Dataset and Sampler
+dataset:
+  _component_: torchtune.datasets.alpaca_cleaned_dataset
+  packed: False  # True increases speed
+seed: null
+shuffle: True
+batch_size: 4
+
+# Optimizer and Scheduler
+optimizer:
+  _component_: torch.optim.AdamW
+  fused: True
+  weight_decay: 0.01
+  lr: 3e-4
+lr_scheduler:
+  _component_: torchtune.training.lr_schedulers.get_cosine_schedule_with_warmup
+  num_warmup_steps: 100
+
+loss:
+  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
+
+# Training
+epochs: 1
+max_steps_per_epoch: null
+gradient_accumulation_steps: 8  # Use to increase virtual batch size
+compile: False  # pytorch compile, set to true for better perf/memory
+
+# Logging
+output_dir: /tmp/qat_lora_finetune_output
+metric_logger:
+  _component_: torchtune.training.metric_logging.DiskLogger
+  log_dir: ${output_dir}
+log_every_n_steps: 1
+log_peak_memory_stats: True
+
+# Environment
+device: cuda
+dtype: bf16
+enable_activation_checkpointing: False  # True reduces memory
+enable_activation_offloading: False  # True reduces memory
+
+# Profiler (disabled)
+profiler:
+  _component_: torchtune.training.setup_torch_profiler
+  enabled: False
+
+  #Output directory of trace artifacts
+  output_dir: ${output_dir}/profiling_outputs
+
+  #`torch.profiler.ProfilerActivity` types to trace
+  cpu: True
+  cuda: True
+
+  #trace options passed to `torch.profiler.profile`
+  profile_memory: False
+  with_stack: False
+  record_shapes: True
+  with_flops: False
+
+  # `torch.profiler.schedule` options:
+  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
+  wait_steps: 5
+  warmup_steps: 3
+  active_steps: 2
+  num_cycles: 1
+
+# QAT arguments
+quantizer:
+  _component_: torchtune.training.quantization.Int8DynActInt4WeightQATQuantizer
+  groupsize: 256