#8142: Update sweep tests

tenstorrent · Oct 22, 2024 · f043670 · f043670
1 parent 544b512
commit f043670
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Showing 4 changed files with 101 additions and 61 deletions.
diff --git a/tests/sweep_framework/sweeps/eltwise/unary/log10/log10.py b/tests/sweep_framework/sweeps/eltwise/unary/log10/log10.py
@@ -22,44 +22,53 @@
         "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
-        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
-        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
 # If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
 def run(
     input_shape,
-    input_a_dtype,
-    input_a_layout,
-    input_a_memory_config,
+    input_dtype,
+    input_layout,
+    input_memory_config,
     output_memory_config,
     *,
     device,
 ) -> list:
     torch.manual_seed(0)
 
-    torch_input_tensor_a = gen_func_with_cast_tt(
-        partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype
+    torch_input_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=1, high=100, dtype=torch.float32), input_dtype
     )(input_shape)
-    torch_output_tensor = torch.log10(torch_input_tensor_a)
-
-    input_tensor_a = ttnn.from_torch(
-        torch_input_tensor_a,
-        dtype=input_a_dtype,
-        layout=input_a_layout,
+    golden_function = ttnn.get_golden_function(ttnn.log10)
+    torch_output_tensor = golden_function(torch_input_tensor)
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
         device=device,
-        memory_config=input_a_memory_config,
+        memory_config=input_memory_config,
     )
 
     start_time = start_measuring_time()
-    result = ttnn.log10(input_tensor_a, memory_config=output_memory_config)
+    result = ttnn.log10(input_tensor, memory_config=output_memory_config)
     output_tensor = ttnn.to_torch(result)
     e2e_perf = stop_measuring_time(start_time)
 

diff --git a/tests/sweep_framework/sweeps/eltwise/unary/log1p/log1p.py b/tests/sweep_framework/sweeps/eltwise/unary/log1p/log1p.py
@@ -22,44 +22,55 @@
         "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
-        "input_a_dtype": [ttnn.bfloat16],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
-        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT or test_vector["input_dtype"] == ttnn.bfloat8_b:
+        return True, "Row Major layout and bfloat8_b are not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
 # If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
 def run(
     input_shape,
-    input_a_dtype,
-    input_a_layout,
-    input_a_memory_config,
+    input_dtype,
+    input_layout,
+    input_memory_config,
     output_memory_config,
     *,
     device,
 ) -> list:
     torch.manual_seed(0)
 
-    torch_input_tensor_a = gen_func_with_cast_tt(
-        partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype
+    torch_input_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=1, high=100, dtype=torch.float32), input_dtype
     )(input_shape)
-    torch_output_tensor = torch.log1p(torch_input_tensor_a)
 
-    input_tensor_a = ttnn.from_torch(
-        torch_input_tensor_a,
-        dtype=input_a_dtype,
-        layout=input_a_layout,
+    golden_function = ttnn.get_golden_function(ttnn.log1p)
+    torch_output_tensor = golden_function(torch_input_tensor)
+
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
         device=device,
-        memory_config=input_a_memory_config,
+        memory_config=input_memory_config,
     )
 
     start_time = start_measuring_time()
-    result = ttnn.log1p(input_tensor_a, memory_config=output_memory_config)
+    result = ttnn.log1p(input_tensor, memory_config=output_memory_config)
     output_tensor = ttnn.to_torch(result)
     e2e_perf = stop_measuring_time(start_time)
 

diff --git a/tests/sweep_framework/sweeps/eltwise/unary/log2/log2.py b/tests/sweep_framework/sweeps/eltwise/unary/log2/log2.py
@@ -22,44 +22,54 @@
         "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
-        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
-        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
 # If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
 def run(
     input_shape,
-    input_a_dtype,
-    input_a_layout,
-    input_a_memory_config,
+    input_dtype,
+    input_layout,
+    input_memory_config,
     output_memory_config,
     *,
     device,
 ) -> list:
     torch.manual_seed(0)
 
-    torch_input_tensor_a = gen_func_with_cast_tt(
-        partial(torch_random, low=1, high=100, dtype=torch.float32), input_a_dtype
+    torch_input_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=1, high=100, dtype=torch.float32), input_dtype
     )(input_shape)
-    torch_output_tensor = torch.log2(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.log2)
+    torch_output_tensor = golden_function(torch_input_tensor)
 
-    input_tensor_a = ttnn.from_torch(
-        torch_input_tensor_a,
-        dtype=input_a_dtype,
-        layout=input_a_layout,
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
         device=device,
-        memory_config=input_a_memory_config,
+        memory_config=input_memory_config,
     )
 
     start_time = start_measuring_time()
-    result = ttnn.log2(input_tensor_a, memory_config=output_memory_config)
+    result = ttnn.log2(input_tensor, memory_config=output_memory_config)
     output_tensor = ttnn.to_torch(result)
     e2e_perf = stop_measuring_time(start_time)
 

diff --git a/tests/sweep_framework/sweeps/eltwise/unary/log_sigmoid/log_sigmoid.py b/tests/sweep_framework/sweeps/eltwise/unary/log_sigmoid/log_sigmoid.py
@@ -22,44 +22,54 @@
         "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
         + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
         + gen_shapes([32, 32], [256, 256], [32, 32], 32),
-        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
-        "input_a_layout": [ttnn.TILE_LAYOUT],
-        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
         "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
     },
 }
 
 
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    return False, None
+
+
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
 # If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
 def run(
     input_shape,
-    input_a_dtype,
-    input_a_layout,
-    input_a_memory_config,
+    input_dtype,
+    input_layout,
+    input_memory_config,
     output_memory_config,
     *,
     device,
 ) -> list:
     torch.manual_seed(0)
 
-    torch_input_tensor_a = gen_func_with_cast_tt(
-        partial(torch_random, low=-4, high=10, dtype=torch.float32), input_a_dtype
+    torch_input_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=-4, high=10, dtype=torch.float32), input_dtype
     )(input_shape)
-    torch_output_tensor = torch.nn.functional.logsigmoid(torch_input_tensor_a)
+    golden_function = ttnn.get_golden_function(ttnn.log_sigmoid)
+    torch_output_tensor = golden_function(torch_input_tensor)
 
-    input_tensor_a = ttnn.from_torch(
-        torch_input_tensor_a,
-        dtype=input_a_dtype,
-        layout=input_a_layout,
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
         device=device,
-        memory_config=input_a_memory_config,
+        memory_config=input_memory_config,
     )
 
     start_time = start_measuring_time()
-    result = ttnn.log_sigmoid(input_tensor_a, memory_config=output_memory_config)
+    result = ttnn.log_sigmoid(input_tensor, memory_config=output_memory_config)
     output_tensor = ttnn.to_torch(result)
     e2e_perf = stop_measuring_time(start_time)