Revert "Model shapes config" (#2114)

Revert "Model shapes config (#2036)"

This reverts commit a724a37.

Author: jerryzh168

benchmarks/microbenchmarks/README.md

@@ -63,74 +63,14 @@ Currently, quantization string is in same format as the one being passed in llam
 
 ### Model Types
 - `linear`: Simple linear layer
-- `ln_linear_<activation>`: LayerNorm + Linear + Activation, where activation can be:
-  - `ln_linear_sigmoid`: LayerNorm + Linear + Sigmoid
-  - `ln_linear_relu`: LayerNorm + Linear + ReLU
-  - `ln_linear_leakyrelu`: LayerNorm + Linear + LeakyReLU
-  - `ln_linear_relu6`: LayerNorm + Linear + ReLU6
-  - `ln_linear_gelu`: LayerNorm + Linear + GELU
-  - `ln_linear_silu`: LayerNorm + Linear + SiLU
-  - `ln_linear_hardswish`: LayerNorm + Linear + Hardswish
-- `transformer_block`: Transformer block with self-attention and MLP
+- `ln_linear_sigmoid`: LayerNorm + Linear + Sigmoid
 
 ### Device Options
 - `cuda`: NVIDIA GPU
 - `xpu`: Intel GPU
 - `mps`: Apple Silicon GPU
 - `cpu`: CPU fallback
 
-### Shape Generation Options
-- `custom`: Manually specify shapes as a list of [m, k, n] dimensions
-  ```yaml
-  matrix_shapes:
-    - name: "custom"
-      shapes: [
-        [1024, 1024, 1024],  # [m, k, n]
-        [2048, 4096, 1024]
-      ]
-  ```
-
-- `llama`: Use LLaMa 2 70B single-node weight shapes (assumes fused attn.wqkv and ffn.w13)
-  - Generates shapes for: "attn.wqkv", "attn.w0", "ffn.w13", "ffn.w2"
-  ```yaml
-  matrix_shapes:
-    - name: "llama"
-  ```
-
-- `pow2`: Generate shapes with dimensions that are powers of 2
-  - Parameters:
-    - `min_power`: Minimum power of 2 (default: 10, which is 1024)
-    - `max_power`: Maximum power of 2 (default: 14, which is 16,384)
-  ```yaml
-  matrix_shapes:
-    - name: "pow2"
-      min_power: 10  # 2^10 = 1024
-      max_power: 12  # 2^12 = 4096
-  ```
-
-- `pow2_extended`: Generate shapes with dimensions that are powers of 2 and powers of 2 + half
-  - Parameters:
-    - `min_power`: Minimum power of 2 (default: 10, which is 1024)
-    - `max_power`: Maximum power of 2 (default: 14, which is 16,384)
-  ```yaml
-  matrix_shapes:
-    - name: "pow2_extended"
-      min_power: 10  # Generates: 1024, 1536, 2048, 3072, etc.
-      max_power: 11
-  ```
-
-- `sweep`: Generate a sweep of shapes with different powers of 2 for M, K, N dimensions
-  - Parameters:
-    - `min_power`: Minimum power of 2 (default: 8, which is 256)
-    - `max_power`: Maximum power of 2 (default: 15, which is 32,768)
-  - Note: This generates all combinations of M, K, N dimensions, which can be a large number of shapes
-  ```yaml
-  matrix_shapes:
-    - name: "sweep"
-      min_power: 8  # 2^8 = 256
-      max_power: 9  # 2^9 = 512
-  ```
-
 ## Output
 
 Results are saved to a CSV file in the specified output directory

benchmarks/microbenchmarks/benchmark_inference.py

@@ -22,14 +22,12 @@
     BenchmarkConfig,
     BenchmarkResult,
     clean_caches,
+    create_model_and_input,
     model_inference_time_in_ms,
     string_to_config,
 )
 from torchao.quantization import quantize_
 from torchao.sparsity.sparse_api import sparsify_
-from torchao.testing.model_architectures import (
-    create_model_and_input_data,
-)
 
 
 def run(config: BenchmarkConfig) -> BenchmarkResult:
@@ -40,7 +38,7 @@ def run(config: BenchmarkConfig) -> BenchmarkResult:
         # Create output directory if it doesn't exist
         Path(config.output_dir).mkdir(parents=True, exist_ok=True)
 
-        base_model, input_data = create_model_and_input_data(
+        base_model, input_data = create_model_and_input(
             config.model_type,
             config.m,
             config.k,

benchmarks/microbenchmarks/benchmark_runner.py

@@ -48,50 +48,9 @@ def get_shapes_for_config(
         name = shape_config["name"]
         if name == "custom":
             shapes.extend([(name, shape) for shape in shape_config["shapes"]])
-        elif name == "llama":
-            # LLaMa 2 70B single-node weight shapes
-            # assumes fused attn.wqkv and ffn.w13
-            bsz, seq_len = 4, 4096
-            M = bsz * seq_len
-            llama_shapes = {
-                "attn.wqkv": (M, 8192, 1280),
-                "attn.w0": (M, 1024, 8192),
-                "ffn.w13": (M, 8192, 7168),
-                "ffn.w2": (M, 3584, 8192),
-            }
-            shapes.extend([(f"{name}_{k}", v) for k, v in llama_shapes.items()])
-        elif name == "pow2":
-            # Generate shapes with dimensions that are powers of 2
-            min_power_of_2 = shape_config.get("min_power", 10)  # 1024
-            max_power_of_2 = shape_config.get("max_power", 14)  # 16,384
-            for idx, power_of_2 in enumerate(range(min_power_of_2, max_power_of_2 + 1)):
-                val = 2**power_of_2
-                shapes.append((f"{name}_{idx}", [val, val, val]))
-        elif name == "pow2_extended":
-            # Generate shapes with dimensions that are powers of 2 and powers of 2 + half
-            min_power_of_2 = shape_config.get("min_power", 10)  # 1024
-            max_power_of_2 = shape_config.get("max_power", 14)  # 16,384
-            for idx, power_of_2 in enumerate(range(min_power_of_2, max_power_of_2 + 1)):
-                val1 = 2**power_of_2
-                val2 = 2**power_of_2 + 2 ** (power_of_2 - 1)
-                shapes.append((f"{name}_{idx * 2}", [val1, val1, val1]))
-                shapes.append((f"{name}_{idx * 2 + 1}", [val2, val2, val2]))
-        elif name == "sweep":
-            # Generate a sweep of shapes with different powers of 2 for M, K, N
-            min_p2 = shape_config.get("min_power", 8)  # 256
-            max_p2 = shape_config.get("max_power", 15)  # 32,768
-            counter = 0
-            for M_p2 in range(min_p2, max_p2 + 1):
-                M = 2**M_p2
-                for K_p2 in range(min_p2, max_p2 + 1):
-                    K = 2**K_p2
-                    for N_p2 in range(min_p2, max_p2 + 1):
-                        N = 2**N_p2
-                        shapes.append((f"{name}_{counter}", [M, K, N]))
-                        counter += 1
         else:
             raise NotImplementedError(
-                f"Shape config {name} not supported. Supported options: custom, llama, pow2, pow2_extended, sweep."
+                f"Shape config {name} not supported. Currently only supports custom shapes."
             )
     return shapes
 

benchmarks/microbenchmarks/test/benchmark_config.yml

@@ -26,48 +26,3 @@ model_params:
     device: "cuda"
     model_type: "linear"
     enable_profiler: true  # Enable profiling for this model
-
-  - name: "ln_linear_sigmoid_cuda"
-    matrix_shapes:
-      - name: "custom"
-        shapes: [
-          [2048, 4096, 1024],
-        ]
-    high_precision_dtype: "torch.bfloat16"
-    use_torch_compile: true
-    torch_compile_mode: "max-autotune"
-    device: "cuda"
-    model_type: "ln_linear_sigmoid"
-    enable_profiler: true
-
-  - name: "bf16_transformer_block"
-    matrix_shapes:
-      - name: "custom"
-        shapes: [
-          [2048, 4096, 1024],  # For transformer_block, k is the hidden dimension
-        ]
-    high_precision_dtype: "torch.bfloat16"
-    use_torch_compile: true
-    torch_compile_mode: "max-autotune"
-    device: "cuda"
-    model_type: "transformer_block" # TODO: Add a custom model (Figure out how to do this, maybe pass a .py file with model definition)
-    enable_profiler: true
-
-  - name: "large_bf16_ln_linear"
-    matrix_shapes:
-      - name: "llama"  # Example of using LLaMa shapes
-      - name: "pow2"  # Example of using power of 2 shapes
-        min_power: 10  # 1024
-        max_power: 12  # 4096
-      - name: "pow2_extended"  # Example of using extended power of 2 shapes
-        min_power: 10  # 1024
-        max_power: 11  # 2048
-      - name: "sweep"  # Example of using sweep shapes (commented out as it generates many shapes)
-        min_power: 8   # 256
-        max_power: 9   # 512
-    high_precision_dtype: "torch.bfloat16"
-    use_torch_compile: true
-    torch_compile_mode: "max-autotune"
-    device: "cuda"
-    model_type: "linear"
-    enable_profiler: true  # Enable profiling for this model

benchmarks/microbenchmarks/test/test_benchmark_profiler.py

@@ -15,8 +15,8 @@
 )
 from benchmarks.microbenchmarks.utils import (
     BenchmarkConfig,
+    ToyLinearModel,
 )
-from torchao.testing.model_architectures import ToyLinearModel
 
 
 class TestBenchmarkProfiler(unittest.TestCase):

benchmarks/microbenchmarks/test/test_benchmark_runner.py

@@ -57,72 +57,12 @@ def tearDown(self):
         shutil.rmtree(self.temp_dir)
 
     def test_get_shapes_for_config(self):
-        # Test custom shapes
         shapes = get_shapes_for_config(
             self.test_config["model_params"][0]["matrix_shapes"]
         )
         self.assertEqual(len(shapes), 1)
         self.assertEqual(shapes[0], ("custom", [1024, 1024, 1024]))
 
-        # Test llama shapes
-        llama_shapes = get_shapes_for_config([{"name": "llama"}])
-        self.assertEqual(len(llama_shapes), 4)  # 4 LLaMa shapes
-        self.assertTrue(
-            any(name.startswith("llama_attn.wqkv") for name, _ in llama_shapes)
-        )
-        self.assertTrue(
-            any(name.startswith("llama_attn.w0") for name, _ in llama_shapes)
-        )
-        self.assertTrue(
-            any(name.startswith("llama_ffn.w13") for name, _ in llama_shapes)
-        )
-        self.assertTrue(
-            any(name.startswith("llama_ffn.w2") for name, _ in llama_shapes)
-        )
-
-        # Test pow2 shapes
-        pow2_shapes = get_shapes_for_config(
-            [{"name": "pow2", "min_power": 10, "max_power": 12}]
-        )
-        self.assertEqual(len(pow2_shapes), 3)  # 3 powers of 2 (10, 11, 12)
-        self.assertEqual(pow2_shapes[0], ("pow2_0", [1024, 1024, 1024]))  # 2^10
-        self.assertEqual(pow2_shapes[1], ("pow2_1", [2048, 2048, 2048]))  # 2^11
-        self.assertEqual(pow2_shapes[2], ("pow2_2", [4096, 4096, 4096]))  # 2^12
-
-        # Test pow2_extended shapes
-        pow2_extended_shapes = get_shapes_for_config(
-            [{"name": "pow2_extended", "min_power": 10, "max_power": 11}]
-        )
-        self.assertEqual(
-            len(pow2_extended_shapes), 4
-        )  # 2 powers of 2, each with 2 variants
-        self.assertEqual(
-            pow2_extended_shapes[0], ("pow2_extended_0", [1024, 1024, 1024])
-        )  # 2^10
-        self.assertEqual(
-            pow2_extended_shapes[1], ("pow2_extended_1", [1536, 1536, 1536])
-        )  # 2^10 + 2^9
-        self.assertEqual(
-            pow2_extended_shapes[2], ("pow2_extended_2", [2048, 2048, 2048])
-        )  # 2^11
-        self.assertEqual(
-            pow2_extended_shapes[3], ("pow2_extended_3", [3072, 3072, 3072])
-        )  # 2^11 + 2^10
-
-        # Test sweep shapes (limited to a small range for testing)
-        sweep_shapes = get_shapes_for_config(
-            [{"name": "sweep", "min_power": 8, "max_power": 9}]
-        )
-        # For min_power=8, max_power=9, we should have 8 shapes (2^3 = 8 combinations)
-        self.assertEqual(len(sweep_shapes), 8)
-        # Check that all shapes have the expected format
-        for name, shape in sweep_shapes:
-            self.assertTrue(name.startswith("sweep_"))
-            self.assertEqual(len(shape), 3)  # [M, K, N]
-            # Check that all dimensions are powers of 2 between 2^8 and 2^9
-            for dim in shape:
-                self.assertTrue(dim in [256, 512])  # 2^8, 2^9
-
     def test_get_param_combinations(self):
         model_param = self.test_config["model_params"][0]
         shapes, params = get_param_combinations(model_param)

benchmarks/microbenchmarks/test/test_utils.py

@@ -16,17 +16,15 @@
     BlockSparseWeightConfig,
     Float8DynamicActivationFloat8SemiSparseWeightConfig,
     Int4WeightOnlyConfig,
+    LNLinearSigmoid,
     SemiSparseWeightConfig,
+    ToyLinearModel,
     clean_caches,
+    create_model_and_input,
     generate_results_csv,
     get_default_device,
     string_to_config,
 )
-from torchao.testing.model_architectures import (
-    LNLinearActivationModel,
-    ToyLinearModel,
-    create_model_and_input_data,
-)
 
 
 class TestUtils(unittest.TestCase):
@@ -155,7 +153,7 @@ def test_toy_linear_model(self):
         self.assertEqual(out.dtype, torch.float32)
 
     def test_ln_linear_sigmoid(self):
-        model = LNLinearActivationModel(fc_dim1=64, fc_dim2=32, dtype=torch.float32)
+        model = LNLinearSigmoid(fc_dim1=64, fc_dim2=32, dtype=torch.float32)
         x = torch.randn(16, 64)
         out = model(x)
         self.assertEqual(out.shape, (16, 32))
@@ -164,9 +162,9 @@ def test_ln_linear_sigmoid(self):
             torch.all((out >= 0) & (out <= 1))
         )  # Check sigmoid output range
 
-    def test_create_model_and_input_data(self):
+    def test_create_model_and_input(self):
         m, k, n = 16, 64, 32
-        model, input_data = create_model_and_input_data(
+        model, input_data = create_model_and_input(
             model_type="linear",
             m=m,
             k=k,
@@ -177,15 +175,15 @@ def test_create_model_and_input_data(self):
         self.assertIsInstance(model, ToyLinearModel)
         self.assertEqual(input_data.shape, (m, k))
 
-        model, input_data = create_model_and_input_data(
+        model, input_data = create_model_and_input(
             model_type="ln_linear_sigmoid",
             m=m,
             k=k,
             n=n,
             high_precision_dtype=torch.float32,
             device="cpu",
         )
-        self.assertIsInstance(model, LNLinearActivationModel)
+        self.assertIsInstance(model, LNLinearSigmoid)
         self.assertEqual(input_data.shape, (m, k))
 
     def test_generate_results_csv(self):