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Posted to commits@tvm.apache.org by tq...@apache.org on 2023/05/06 13:14:42 UTC
[tvm-site] branch asf-site updated: deploying docs (apache/tvm@6c53d85f797f694f5ee21b85b19df3fbba71bb47)
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new ec80aea36e deploying docs (apache/tvm@6c53d85f797f694f5ee21b85b19df3fbba71bb47)
ec80aea36e is described below
commit ec80aea36e853e6f8bb89848fa8e4207b386ec8b
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Sat May 6 13:14:20 2023 +0000
deploying docs (apache/tvm@6c53d85f797f694f5ee21b85b19df3fbba71bb47)
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docs/tutorials/frontend/deploy_ssd_gluoncv.html | 10 -
6501 files changed, 1023920 insertions(+), 619190 deletions(-)
diff --git a/docs/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py b/docs/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py
index 3d810b25fe..a9c45c15ce 100644
--- a/docs/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py
+++ b/docs/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py
@@ -98,6 +98,7 @@ B_np = np.random.randn(M, N).astype("float32")
Y_np = Y_np + B_np # Bias add
Y_np = np.maximum(np.zeros((M, N), dtype="float32"), Y_np) # Relu
+
######################################################################
# Create the search task
# ^^^^^^^^^^^^^^^^^^^^^^
@@ -229,6 +230,7 @@ search_policy = auto_scheduler.SketchPolicy(
],
)
+
######################################################################
# Run the search
# ^^^^^^^^^^^^^^
@@ -237,52 +239,55 @@ search_policy = auto_scheduler.SketchPolicy(
# After some measurement trials, we can load the best schedule from the log
# file and apply it.
-# Run auto-tuning (search)
-# Notice: We do not run the tuning in our webpage server since it takes too long.
-# Uncomment the following line to run it by yourself.
-task.tune(tune_option, search_policy)
-# Apply the best schedule
-sch, args = task.apply_best(log_file)
+def tune_and_evaluate(tune_option, search_policy):
+ # Run auto-tuning (search)
+ task.tune(tune_option, search_policy)
-######################################################################
-# We can lower the schedule to see the IR after auto-scheduling.
-# The auto-scheduler correctly performs optimizations including multi-level tiling,
-# layout transformation, parallelization, vectorization, unrolling, and operator fusion.
-
-print("Lowered TIR:")
-print(tvm.lower(sch, args, simple_mode=True))
+ # Apply the best schedule
+ sch, args = task.apply_best(log_file)
-######################################################################
-# Check correctness and evaluate performance
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-# We build the binary and check its correctness and performance.
+ # We can lower the schedule to see the IR after auto-scheduling.
+ # The auto-scheduler correctly performs optimizations including multi-level tiling,
+ # layout transformation, parallelization, vectorization, unrolling, and operator fusion.
+ print("Lowered TIR:")
+ print(tvm.lower(sch, args, simple_mode=True))
-func = tvm.build(sch, args, target)
+ # Check correctness and evaluate performance
+ # We build the binary and check its correctness and performance.
+ func = tvm.build(sch, args, target)
-dev = tvm.cpu()
+ dev = tvm.cpu()
-X_tvm = tvm.nd.array(X_np, device=dev)
-W_data_tvm = tvm.nd.array(W_sp_np.data, device=dev)
-W_indices_tvm = tvm.nd.array(W_sp_np.indices, device=dev)
-W_indptr_tvm = tvm.nd.array(W_sp_np.indptr, device=dev)
-B_tvm = tvm.nd.array(B_np, device=dev)
-Y_tvm = tvm.nd.empty(Y_np.shape, device=dev)
+ X_tvm = tvm.nd.array(X_np, device=dev)
+ W_data_tvm = tvm.nd.array(W_sp_np.data, device=dev)
+ W_indices_tvm = tvm.nd.array(W_sp_np.indices, device=dev)
+ W_indptr_tvm = tvm.nd.array(W_sp_np.indptr, device=dev)
+ B_tvm = tvm.nd.array(B_np, device=dev)
+ Y_tvm = tvm.nd.empty(Y_np.shape, device=dev)
-func(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm)
+ func(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm)
-# Check results
-tvm.testing.assert_allclose(Y_np, Y_tvm.numpy(), atol=1e-4, rtol=1e-4)
+ # Check results
+ tvm.testing.assert_allclose(Y_np, Y_tvm.numpy(), atol=1e-4, rtol=1e-4)
-# Evaluate execution time.
-evaluator = func.time_evaluator(func.entry_name, dev, min_repeat_ms=500)
-print(
- "Execution time of this operator: %.3f ms"
- % (
- np.median(evaluator(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm).results)
- * 1000
+ # Evaluate execution time.
+ evaluator = func.time_evaluator(func.entry_name, dev, min_repeat_ms=500)
+ print(
+ "Execution time of this operator: %.3f ms"
+ % (
+ np.median(
+ evaluator(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm).results
+ )
+ * 1000
+ )
)
-)
+
+
+# Notice: We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+# tune_and_evaluate(tune_option, search_policy)
+
######################################################################
# .. note:: Tuning result example
diff --git a/docs/_downloads/09df7d9b9c90a2a1bdd570520693fd9f/micro_pytorch.ipynb b/docs/_downloads/09df7d9b9c90a2a1bdd570520693fd9f/micro_pytorch.ipynb
index 718bc8516d..f5cc7aac69 100644
--- a/docs/_downloads/09df7d9b9c90a2a1bdd570520693fd9f/micro_pytorch.ipynb
+++ b/docs/_downloads/09df7d9b9c90a2a1bdd570520693fd9f/micro_pytorch.ipynb
@@ -172,7 +172,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb b/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
index 8ec08a8ee3..f1094e81bf 100644
--- a/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
+++ b/docs/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
@@ -179,7 +179,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb b/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
index 143634f2bf..d1cb71310b 100644
--- a/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
+++ b/docs/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
@@ -301,7 +301,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb b/docs/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb
index 57e2871dcb..139edcc81f 100644
--- a/docs/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb
+++ b/docs/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb
@@ -240,7 +240,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb b/docs/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb
index b582802fe8..0e8080ee07 100644
--- a/docs/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb
+++ b/docs/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb
@@ -118,7 +118,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb b/docs/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb
index 0bc0f835d1..1a79443de9 100644
--- a/docs/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb
+++ b/docs/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb
@@ -311,7 +311,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb b/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
index 679acc80ee..fd741f45d2 100644
--- a/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
+++ b/docs/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
@@ -161,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb b/docs/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb
index a7938aa85d..38559dfa02 100644
--- a/docs/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb
+++ b/docs/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb
@@ -303,7 +303,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb b/docs/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb
index a8d9c39eba..299d97fbfa 100644
--- a/docs/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb
+++ b/docs/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb
@@ -172,7 +172,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/1fc1683d67bee4f26703504a58d42578/micro_mlperftiny.ipynb b/docs/_downloads/1fc1683d67bee4f26703504a58d42578/micro_mlperftiny.ipynb
index ff5287c9d8..4a7e63f787 100644
--- a/docs/_downloads/1fc1683d67bee4f26703504a58d42578/micro_mlperftiny.ipynb
+++ b/docs/_downloads/1fc1683d67bee4f26703504a58d42578/micro_mlperftiny.ipynb
@@ -222,7 +222,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/23968bb778cd9591b7ad858bf17dcc3e/from_tflite.ipynb b/docs/_downloads/23968bb778cd9591b7ad858bf17dcc3e/from_tflite.ipynb
index 9957ace9a6..1db6f41f02 100644
--- a/docs/_downloads/23968bb778cd9591b7ad858bf17dcc3e/from_tflite.ipynb
+++ b/docs/_downloads/23968bb778cd9591b7ad858bf17dcc3e/from_tflite.ipynb
@@ -161,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb b/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb
index 3ac5f65419..79f6ab23c2 100644
--- a/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb
+++ b/docs/_downloads/246d4b8509474fd9046e69f6cc9b7f87/auto_scheduler_matmul_x86.ipynb
@@ -197,7 +197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/293f8d0753933b706a0b588f909fe38a/tune_sparse_x86.ipynb b/docs/_downloads/293f8d0753933b706a0b588f909fe38a/tune_sparse_x86.ipynb
index a1cd6d0114..47a7f24c2e 100644
--- a/docs/_downloads/293f8d0753933b706a0b588f909fe38a/tune_sparse_x86.ipynb
+++ b/docs/_downloads/293f8d0753933b706a0b588f909fe38a/tune_sparse_x86.ipynb
@@ -134,43 +134,7 @@
},
"outputs": [],
"source": [
- "# Run auto-tuning (search)\n# Notice: We do not run the tuning in our webpage server since it takes too long.\n# Uncomment the following line to run it by yourself.\ntask.tune(tune_option, search_policy)\n\n# Apply the best schedule\nsch, args = task.apply_best(log_file)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can lower the schedule to see the IR after auto-scheduling.\nThe auto-scheduler correctly performs optimizations including multi-level tiling,\nlayout transformation, parallelization, vectorization, unrolling, and operator fusion.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "print(\"Lowered TIR:\")\nprint(tvm.lower(sch, args, simple_mode=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Check correctness and evaluate performance\nWe build the binary and check its correctness and performance.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "func = tvm.build(sch, args, target)\n\ndev = tvm.cpu()\n\nX_tvm = tvm.nd.array(X_np, device=dev)\nW_data_tvm = tvm.nd.array(W_sp_np.data, device=dev)\nW_indices_tvm = tvm.nd.array(W_sp_np.indices, device=dev)\nW_indptr_tvm = tvm.nd.array(W_sp_np.indptr, device=dev)\nB_tvm = tvm.nd.array(B_np, device=dev)\nY_tvm = tvm.nd.empty(Y_np.shape, device=dev)\n\nfunc(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm)\n\n# Check results\ntvm.testing.assert_allclose(Y_np, Y_tvm.n [...]
+ "def tune_and_evaluate(tune_option, search_policy):\n # Run auto-tuning (search)\n task.tune(tune_option, search_policy)\n\n # Apply the best schedule\n sch, args = task.apply_best(log_file)\n\n # We can lower the schedule to see the IR after auto-scheduling.\n # The auto-scheduler correctly performs optimizations including multi-level tiling,\n # layout transformation, parallelization, vectorization, unrolling, and operator fusion.\n print(\"Lowered TIR:\ [...]
]
},
{
@@ -197,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/2a4c6a9cfa43e8afef159a2bf1b99108/install.ipynb b/docs/_downloads/2a4c6a9cfa43e8afef159a2bf1b99108/install.ipynb
index 619216f49d..b1de0e985a 100644
--- a/docs/_downloads/2a4c6a9cfa43e8afef159a2bf1b99108/install.ipynb
+++ b/docs/_downloads/2a4c6a9cfa43e8afef159a2bf1b99108/install.ipynb
@@ -49,7 +49,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/2e7b51cb39c472626dd3f046d9b89966/from_oneflow.ipynb b/docs/_downloads/2e7b51cb39c472626dd3f046d9b89966/from_oneflow.ipynb
index 0b833200f5..26c49a8a65 100644
--- a/docs/_downloads/2e7b51cb39c472626dd3f046d9b89966/from_oneflow.ipynb
+++ b/docs/_downloads/2e7b51cb39c472626dd3f046d9b89966/from_oneflow.ipynb
@@ -172,7 +172,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/2f91b1346a0ba21b800081aa15fdaac2/autotvm_relay_x86.ipynb b/docs/_downloads/2f91b1346a0ba21b800081aa15fdaac2/autotvm_relay_x86.ipynb
index 8941225d36..6ff1575a1e 100644
--- a/docs/_downloads/2f91b1346a0ba21b800081aa15fdaac2/autotvm_relay_x86.ipynb
+++ b/docs/_downloads/2f91b1346a0ba21b800081aa15fdaac2/autotvm_relay_x86.ipynb
@@ -340,7 +340,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb b/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb
index eca8a86119..c9424e7e80 100644
--- a/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb
+++ b/docs/_downloads/37bbf9e2065ec8deeb64a8d9fa0755bc/autotvm_matmul_x86.ipynb
@@ -214,7 +214,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/399e1d7889ca66b69d51655784827503/deploy_object_detection_pytorch.ipynb b/docs/_downloads/399e1d7889ca66b69d51655784827503/deploy_object_detection_pytorch.ipynb
index 20fd2ef2d0..525532b6c3 100644
--- a/docs/_downloads/399e1d7889ca66b69d51655784827503/deploy_object_detection_pytorch.ipynb
+++ b/docs/_downloads/399e1d7889ca66b69d51655784827503/deploy_object_detection_pytorch.ipynb
@@ -154,7 +154,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/3b5e41b16a898b72d18127ebe2182c66/tensorize.ipynb b/docs/_downloads/3b5e41b16a898b72d18127ebe2182c66/tensorize.ipynb
index 98fd44e5ff..d8a227e9b7 100644
--- a/docs/_downloads/3b5e41b16a898b72d18127ebe2182c66/tensorize.ipynb
+++ b/docs/_downloads/3b5e41b16a898b72d18127ebe2182c66/tensorize.ipynb
@@ -233,7 +233,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb b/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb
index 6f9b2c1e11..f7eb6b4b03 100644
--- a/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb
+++ b/docs/_downloads/3dd2108354ac3028c96bcd6a0c7899dd/relay_quick_start.ipynb
@@ -136,7 +136,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb b/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb
index 3bd51cd5cb..2c5cbd8571 100644
--- a/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb
+++ b/docs/_downloads/4459ebf5b03d332f7f380abdaef81c05/tensor_expr_get_started.ipynb
@@ -602,7 +602,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/4bbcfcce3c35b0b795a42c998ceb3770/from_mxnet.ipynb b/docs/_downloads/4bbcfcce3c35b0b795a42c998ceb3770/from_mxnet.ipynb
index 331ebb72ca..e7bd7ee38f 100644
--- a/docs/_downloads/4bbcfcce3c35b0b795a42c998ceb3770/from_mxnet.ipynb
+++ b/docs/_downloads/4bbcfcce3c35b0b795a42c998ceb3770/from_mxnet.ipynb
@@ -172,7 +172,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/4d3f955a709b320db0d42740fead8ac1/matrix_multiply_opt.ipynb b/docs/_downloads/4d3f955a709b320db0d42740fead8ac1/matrix_multiply_opt.ipynb
index fec4acbd76..e5fc3a4830 100644
--- a/docs/_downloads/4d3f955a709b320db0d42740fead8ac1/matrix_multiply_opt.ipynb
+++ b/docs/_downloads/4d3f955a709b320db0d42740fead8ac1/matrix_multiply_opt.ipynb
@@ -168,7 +168,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/4dc30a43f3a6aa3ed4bc3077ad35ff70/tune_network_arm.ipynb b/docs/_downloads/4dc30a43f3a6aa3ed4bc3077ad35ff70/tune_network_arm.ipynb
index 97b5d061d3..eb76f22f7d 100644
--- a/docs/_downloads/4dc30a43f3a6aa3ed4bc3077ad35ff70/tune_network_arm.ipynb
+++ b/docs/_downloads/4dc30a43f3a6aa3ed4bc3077ad35ff70/tune_network_arm.ipynb
@@ -153,7 +153,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/55a9eff88b1303e525d53269eeb16897/micro_ethosu.ipynb b/docs/_downloads/55a9eff88b1303e525d53269eeb16897/micro_ethosu.ipynb
index 3ae36341ae..809ca7a0cb 100644
--- a/docs/_downloads/55a9eff88b1303e525d53269eeb16897/micro_ethosu.ipynb
+++ b/docs/_downloads/55a9eff88b1303e525d53269eeb16897/micro_ethosu.ipynb
@@ -168,7 +168,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb b/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb
index 00e45dc3ce..2cc4643420 100644
--- a/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb
+++ b/docs/_downloads/5b279d8a8718816263fa65b0eef1a5c0/micro_tflite.ipynb
@@ -244,7 +244,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/5e4e499c097b16a90c517e630502253a/tune_network_mali.ipynb b/docs/_downloads/5e4e499c097b16a90c517e630502253a/tune_network_mali.ipynb
index 4ec99f43d3..2fcd98ccb6 100644
--- a/docs/_downloads/5e4e499c097b16a90c517e630502253a/tune_network_mali.ipynb
+++ b/docs/_downloads/5e4e499c097b16a90c517e630502253a/tune_network_mali.ipynb
@@ -146,7 +146,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/5f1f7bd7d90710fd404f7bcdc4965622/tune_conv2d_layer_cuda.ipynb b/docs/_downloads/5f1f7bd7d90710fd404f7bcdc4965622/tune_conv2d_layer_cuda.ipynb
index 52977a93c6..f16e0fe4e4 100644
--- a/docs/_downloads/5f1f7bd7d90710fd404f7bcdc4965622/tune_conv2d_layer_cuda.ipynb
+++ b/docs/_downloads/5f1f7bd7d90710fd404f7bcdc4965622/tune_conv2d_layer_cuda.ipynb
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "# Run auto-tuning (search)\ntask.tune(tune_option)\n# Apply the best schedule\nsch, args = task.apply_best(log_file)\n\n# Kill the measurement process\ndel measure_ctx"
+ "# Run auto-tuning (search)\n# We do not run the tuning in our webpage server since it takes too long.\n# Uncomment the following line to run it by yourself.\n# task.tune(tune_option)\n# Apply the best schedule\nsch, args = task.apply_best(log_file)\n\n# Kill the measurement process\ndel measure_ctx"
]
},
{
@@ -177,7 +177,7 @@
},
"outputs": [],
"source": [
- "def resume_search(task, log_file):\n print(\"Resume search:\")\n cost_model = auto_scheduler.XGBModel()\n cost_model.update_from_file(log_file)\n search_policy = auto_scheduler.SketchPolicy(\n task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]\n )\n measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=5,\n runner=mea [...]
+ "def resume_search(task, log_file):\n print(\"Resume search:\")\n cost_model = auto_scheduler.XGBModel()\n cost_model.update_from_file(log_file)\n search_policy = auto_scheduler.SketchPolicy(\n task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]\n )\n measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)\n tune_option = auto_scheduler.TuningOptions(\n num_measure_trials=5,\n runner=mea [...]
]
}
],
@@ -197,7 +197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb b/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb
index a4c7e8bd56..6b4a610ffa 100644
--- a/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb
+++ b/docs/_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb
@@ -154,7 +154,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb b/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb
index ce4519e8c0..1a20c56eef 100644
--- a/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb
+++ b/docs/_downloads/63f9e50204143ea3c2d3593c72439b3d/intro_topi.ipynb
@@ -222,7 +222,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/66e1a42229aae7ed49ac268f520e6727/deploy_detection.ipynb b/docs/_downloads/66e1a42229aae7ed49ac268f520e6727/deploy_detection.ipynb
index 0adfe83025..134d9e0665 100644
--- a/docs/_downloads/66e1a42229aae7ed49ac268f520e6727/deploy_detection.ipynb
+++ b/docs/_downloads/66e1a42229aae7ed49ac268f520e6727/deploy_detection.ipynb
@@ -161,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py b/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py
index d7047a0afb..503977aed5 100644
--- a/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py
+++ b/docs/_downloads/6ad550da5092845382b1197f58a93816/tune_conv2d_cuda.py
@@ -202,28 +202,34 @@ measure_option = autotvm.measure_option(
runner=autotvm.LocalRunner(repeat=3, min_repeat_ms=100, timeout=4),
)
+record_file = None
# Begin tuning, log records to file `conv2d.log`
# During tuning we will also try many invalid configs, so you are expected to
# see many error reports. As long as you can see non-zero GFLOPS, it is okay.
-tuner = autotvm.tuner.XGBTuner(task)
-tuner.tune(
- n_trial=20,
- measure_option=measure_option,
- callbacks=[autotvm.callback.log_to_file("conv2d.log")],
-)
+
+# We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following lines to run it by yourself.
+
+# tuner = autotvm.tuner.XGBTuner(task)
+# record_file = "conv2d.log"
+# tuner.tune(
+# n_trial=5,
+# measure_option=measure_option,
+# callbacks=[autotvm.callback.log_to_file(record_file)],
+# )
#########################################################################
# Finally we can inspect the best config from log file, check correctness,
# and measure running time.
# inspect the best config
-dispatch_context = autotvm.apply_history_best("conv2d.log")
+dispatch_context = autotvm.apply_history_best(record_file)
best_config = dispatch_context.query(task.target, task.workload)
print("\nBest config:")
print(best_config)
# apply history best from log file
-with autotvm.apply_history_best("conv2d.log"):
+with autotvm.apply_history_best(record_file):
with tvm.target.Target("cuda"):
s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides, padding)
func = tvm.build(s, arg_bufs)
diff --git a/docs/_downloads/6b0f549107f73f2e48c894372be08bcb/tune_relay_mobile_gpu.ipynb b/docs/_downloads/6b0f549107f73f2e48c894372be08bcb/tune_relay_mobile_gpu.ipynb
index 0844d48aa0..bb9225cc59 100644
--- a/docs/_downloads/6b0f549107f73f2e48c894372be08bcb/tune_relay_mobile_gpu.ipynb
+++ b/docs/_downloads/6b0f549107f73f2e48c894372be08bcb/tune_relay_mobile_gpu.ipynb
@@ -160,7 +160,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/6e0673ce1f08636c34d0b9a73ea114f7/uma.ipynb b/docs/_downloads/6e0673ce1f08636c34d0b9a73ea114f7/uma.ipynb
index e873d3033f..a1cbeee25a 100644
--- a/docs/_downloads/6e0673ce1f08636c34d0b9a73ea114f7/uma.ipynb
+++ b/docs/_downloads/6e0673ce1f08636c34d0b9a73ea114f7/uma.ipynb
@@ -203,7 +203,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/6e511f5a8ddbf12f2fca2dfadc0cc4a9/micro_tvmc.ipynb b/docs/_downloads/6e511f5a8ddbf12f2fca2dfadc0cc4a9/micro_tvmc.ipynb
index f558732fb6..9b87c8bdde 100644
--- a/docs/_downloads/6e511f5a8ddbf12f2fca2dfadc0cc4a9/micro_tvmc.ipynb
+++ b/docs/_downloads/6e511f5a8ddbf12f2fca2dfadc0cc4a9/micro_tvmc.ipynb
@@ -120,7 +120,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb b/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb
index 65ff632d40..b84c4cca5b 100644
--- a/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb
+++ b/docs/_downloads/729378592a96230b4f7be71b44da43a4/scan.ipynb
@@ -161,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb b/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb
index fe57e3b5a7..b04bba3d7f 100644
--- a/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb
+++ b/docs/_downloads/732ed130cbc15432e737da8cc47e1734/tune_conv2d_cuda.ipynb
@@ -69,7 +69,7 @@
},
"outputs": [],
"source": [
- "# logging config (for printing tuning log to screen)\nlogging.getLogger(\"autotvm\").setLevel(logging.DEBUG)\nlogging.getLogger(\"autotvm\").addHandler(logging.StreamHandler(sys.stdout))\n\n# the last layer in resnet\nN, H, W, CO, CI, KH, KW, strides, padding = 1, 7, 7, 512, 512, 3, 3, (1, 1), (1, 1)\ntask = autotvm.task.create(\n \"tutorial/conv2d_no_batching\", args=(N, H, W, CO, CI, KH, KW, strides, padding), target=\"cuda\"\n)\nprint(task.config_space)\n\n# Use local gpu, [...]
+ "# logging config (for printing tuning log to screen)\nlogging.getLogger(\"autotvm\").setLevel(logging.DEBUG)\nlogging.getLogger(\"autotvm\").addHandler(logging.StreamHandler(sys.stdout))\n\n# the last layer in resnet\nN, H, W, CO, CI, KH, KW, strides, padding = 1, 7, 7, 512, 512, 3, 3, (1, 1), (1, 1)\ntask = autotvm.task.create(\n \"tutorial/conv2d_no_batching\", args=(N, H, W, CO, CI, KH, KW, strides, padding), target=\"cuda\"\n)\nprint(task.config_space)\n\n# Use local gpu, [...]
]
},
{
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "# inspect the best config\ndispatch_context = autotvm.apply_history_best(\"conv2d.log\")\nbest_config = dispatch_context.query(task.target, task.workload)\nprint(\"\\nBest config:\")\nprint(best_config)\n\n# apply history best from log file\nwith autotvm.apply_history_best(\"conv2d.log\"):\n with tvm.target.Target(\"cuda\"):\n s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides, padding)\n func = tvm.build(s, arg_bufs)\n\n# check correctness\na_np [...]
+ "# inspect the best config\ndispatch_context = autotvm.apply_history_best(record_file)\nbest_config = dispatch_context.query(task.target, task.workload)\nprint(\"\\nBest config:\")\nprint(best_config)\n\n# apply history best from log file\nwith autotvm.apply_history_best(record_file):\n with tvm.target.Target(\"cuda\"):\n s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides, padding)\n func = tvm.build(s, arg_bufs)\n\n# check correctness\na_np = np.r [...]
]
}
],
@@ -107,7 +107,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb b/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb
index 40e64b99d5..75ac3da860 100644
--- a/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb
+++ b/docs/_downloads/7455981870c23c8c76482dedf33d8a42/opt_conv_tensorcore.ipynb
@@ -157,7 +157,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/779f52a44f2b8ab22dc21eee0c27fd4d/from_onnx.ipynb b/docs/_downloads/779f52a44f2b8ab22dc21eee0c27fd4d/from_onnx.ipynb
index 0c3b3a29d2..e69b0d54fa 100644
--- a/docs/_downloads/779f52a44f2b8ab22dc21eee0c27fd4d/from_onnx.ipynb
+++ b/docs/_downloads/779f52a44f2b8ab22dc21eee0c27fd4d/from_onnx.ipynb
@@ -161,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/7c392f39b90d93406ef30c6185c5686c/deploy_model_on_rasp.ipynb b/docs/_downloads/7c392f39b90d93406ef30c6185c5686c/deploy_model_on_rasp.ipynb
index 14f9a5c11f..f5e3eeceed 100644
--- a/docs/_downloads/7c392f39b90d93406ef30c6185c5686c/deploy_model_on_rasp.ipynb
+++ b/docs/_downloads/7c392f39b90d93406ef30c6185c5686c/deploy_model_on_rasp.ipynb
@@ -193,7 +193,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb b/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb
index 4e3d70da56..5a0d3f831b 100644
--- a/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb
+++ b/docs/_downloads/7ef14586a3b62fe120d97d5fedf72879/use_pass_infra.ipynb
@@ -233,7 +233,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb b/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb
index 5272ced86a..de92ecc86a 100644
--- a/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb
+++ b/docs/_downloads/825671e45a9bdc4733400384984cd9dd/build_gcn.ipynb
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "%%shell\npip install torch==1.9.0\npip install dgl==v0.7.2 -f https://data.dgl.ai/wheels/repo.html"
+ "%%shell\npip install torch==2.0.0\npip install dgl==v1.0.0"
]
},
{
@@ -69,7 +69,7 @@
},
"outputs": [],
"source": [
- "from dgl.data import load_data\nfrom collections import namedtuple\n\n\ndef load_dataset(dataset=\"cora\"):\n args = namedtuple(\"args\", [\"dataset\"])\n data = load_data(args(dataset))\n\n # Remove self-loops to avoid duplicate passing of a node's feature to itself\n g = data.graph\n g.remove_edges_from(nx.selfloop_edges(g))\n g.add_edges_from(zip(g.nodes, g.nodes))\n\n return g, data\n\n\ndef evaluate(data, logits):\n test_mask = data.test_mask # the [...]
+ "from dgl.data import load_data\nfrom collections import namedtuple\n\n\ndef evaluate(g, logits):\n label = g.ndata[\"label\"]\n test_mask = g.ndata[\"test_mask\"]\n\n pred = logits.argmax(axis=1)\n acc = (torch.Tensor(pred[test_mask]) == label[test_mask]).float().mean()\n\n return acc"
]
},
{
@@ -87,7 +87,7 @@
},
"outputs": [],
"source": [
- "\"\"\"\nParameters\n----------\ndataset: str\n Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed'].\n\nnum_layer: int\n number of hidden layers\n\nnum_hidden: int\n number of the hidden units in the hidden layer\n\ninfeat_dim: int\n dimension of the input features\n\nnum_classes: int\n dimension of model output (Number of classes)\n\"\"\"\n\ndataset = \"cora\"\ng, data = load_dataset(dataset)\n\nnum_layers = 1\nnum_hidden = 16\ninfeat_dim = data.fe [...]
+ "\"\"\"\nParameters\n----------\nnum_layer: int\n number of hidden layers\n\nnum_hidden: int\n number of the hidden units in the hidden layer\n\ninfeat_dim: int\n dimension of the input features\n\nnum_classes: int\n dimension of model output (Number of classes)\n\"\"\"\n\ndataset = dgl.data.CoraGraphDataset()\ndgl_g = dataset[0]\nnum_layers = 1\nnum_hidden = 16\nfeatures = dgl_g.ndata[\"feat\"]\ninfeat_dim = features.shape[1]\nnum_classes = dataset.num_classes"
]
},
{
@@ -105,7 +105,7 @@
},
"outputs": [],
"source": [
- "from tvm.contrib.download import download_testdata\nfrom dgl import DGLGraph\n\nfeatures = torch.FloatTensor(data.features)\ndgl_g = DGLGraph(g)\n\ntorch_model = GCN(dgl_g, infeat_dim, num_hidden, num_classes, num_layers, F.relu)\n\n# Download the pretrained weights\nmodel_url = \"https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_%s.torch\" % (dataset)\nmodel_path = download_testdata(model_url, \"gcn_%s.pickle\" % (dataset), module=\"gcn_model\")\n\n# Load the weights [...]
+ "from tvm.contrib.download import download_testdata\n\nfeatures = torch.FloatTensor(features)\n\ntorch_model = GCN(dgl_g, infeat_dim, num_hidden, num_classes, num_layers, F.relu)\n\n# Download the pretrained weights\nmodel_url = \"https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_cora.torch\"\nmodel_path = download_testdata(model_url, \"gcn_cora.pickle\", module=\"gcn_model\")\n\n# Load the weights into the model\ntorch_model.load_state_dict(torch.load(model_path))"
]
},
{
@@ -123,7 +123,7 @@
},
"outputs": [],
"source": [
- "torch_model.eval()\nwith torch.no_grad():\n logits_torch = torch_model(features)\nprint(\"Print the first five outputs from DGL-PyTorch execution\\n\", logits_torch[:5])\n\nacc = evaluate(data, logits_torch.numpy())\nprint(\"Test accuracy of DGL results: {:.2%}\".format(acc))"
+ "torch_model.eval()\nwith torch.no_grad():\n logits_torch = torch_model(features)\nprint(\"Print the first five outputs from DGL-PyTorch execution\\n\", logits_torch[:5])\n\nacc = evaluate(dgl_g, logits_torch.numpy())\nprint(\"Test accuracy of DGL results: {:.2%}\".format(acc))"
]
},
{
@@ -159,7 +159,7 @@
},
"outputs": [],
"source": [
- "import numpy as np\nimport networkx as nx\n\n\ndef prepare_params(g, data):\n params = {}\n params[\"infeats\"] = data.features.numpy().astype(\n \"float32\"\n ) # Only support float32 as feature for now\n\n # Generate adjacency matrix\n adjacency = nx.to_scipy_sparse_matrix(g)\n params[\"g_data\"] = adjacency.data.astype(\"float32\")\n params[\"indices\"] = adjacency.indices.astype(\"int32\")\n params[\"indptr\"] = adjacency.indptr.astype(\"int32 [...]
+ "import numpy as np\nimport networkx as nx\n\n\ndef prepare_params(g):\n params = {}\n params[\"infeats\"] = g.ndata[\"feat\"].numpy().astype(\"float32\")\n\n # Generate adjacency matrix\n nx_graph = dgl.to_networkx(g)\n adjacency = nx.to_scipy_sparse_array(nx_graph)\n params[\"g_data\"] = adjacency.data.astype(\"float32\")\n params[\"indices\"] = adjacency.indices.astype(\"int32\")\n params[\"indptr\"] = adjacency.indptr.astype(\"int32\")\n\n # Normali [...]
]
},
{
@@ -177,7 +177,7 @@
},
"outputs": [],
"source": [
- "# Define input features, norms, adjacency matrix in Relay\ninfeats = relay.var(\"infeats\", shape=data.features.shape)\nnorm = relay.Constant(tvm.nd.array(params[\"norm\"]))\ng_data = relay.Constant(tvm.nd.array(params[\"g_data\"]))\nindices = relay.Constant(tvm.nd.array(params[\"indices\"]))\nindptr = relay.Constant(tvm.nd.array(params[\"indptr\"]))\n\nAdjacency = namedtuple(\"Adjacency\", [\"data\", \"indices\", \"indptr\"])\nadj = Adjacency(g_data, indices, indptr)\n\n# Const [...]
+ "# Define input features, norms, adjacency matrix in Relay\ninfeats = relay.var(\"infeats\", shape=features.shape)\nnorm = relay.Constant(tvm.nd.array(params[\"norm\"]))\ng_data = relay.Constant(tvm.nd.array(params[\"g_data\"]))\nindices = relay.Constant(tvm.nd.array(params[\"indices\"]))\nindptr = relay.Constant(tvm.nd.array(params[\"indptr\"]))\n\nAdjacency = namedtuple(\"Adjacency\", [\"data\", \"indices\", \"indptr\"])\nadj = Adjacency(g_data, indices, indptr)\n\n# Construct [...]
]
},
{
@@ -213,7 +213,7 @@
},
"outputs": [],
"source": [
- "m.run()\nlogits_tvm = m.get_output(0).numpy()\nprint(\"Print the first five outputs from TVM execution\\n\", logits_tvm[:5])\n\nlabels = data.labels\ntest_mask = data.test_mask\n\nacc = evaluate(data, logits_tvm)\nprint(\"Test accuracy of TVM results: {:.2%}\".format(acc))\n\nimport tvm.testing\n\n# Verify the results with the DGL model\ntvm.testing.assert_allclose(logits_torch, logits_tvm, atol=1e-3)"
+ "m.run()\nlogits_tvm = m.get_output(0).numpy()\nprint(\"Print the first five outputs from TVM execution\\n\", logits_tvm[:5])\n\nacc = evaluate(dgl_g, logits_tvm)\nprint(\"Test accuracy of TVM results: {:.2%}\".format(acc))\n\nimport tvm.testing\n\n# Verify the results with the DGL model\ntvm.testing.assert_allclose(logits_torch, logits_tvm, atol=1e-3)"
]
}
],
@@ -233,7 +233,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/83b9961c758069912464db3443fffc06/vta_get_started.ipynb b/docs/_downloads/83b9961c758069912464db3443fffc06/vta_get_started.ipynb
index 4ed64a4821..1e3929b317 100644
--- a/docs/_downloads/83b9961c758069912464db3443fffc06/vta_get_started.ipynb
+++ b/docs/_downloads/83b9961c758069912464db3443fffc06/vta_get_started.ipynb
@@ -365,7 +365,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/83e3b018e8bac8d31bb331d200a33a04/from_tensorflow.ipynb b/docs/_downloads/83e3b018e8bac8d31bb331d200a33a04/from_tensorflow.ipynb
index 65a02aa049..3069a461f1 100644
--- a/docs/_downloads/83e3b018e8bac8d31bb331d200a33a04/from_tensorflow.ipynb
+++ b/docs/_downloads/83e3b018e8bac8d31bb331d200a33a04/from_tensorflow.ipynb
@@ -226,7 +226,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb b/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb
index 6c5cc796dd..73474ec254 100644
--- a/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb
+++ b/docs/_downloads/8472bea81cf679760d7e4e77e895726f/extern_op.ipynb
@@ -125,7 +125,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb b/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb
index a8544087aa..7539a6cc96 100644
--- a/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb
+++ b/docs/_downloads/854257a66df713b1f3f82eb3577f95e3/opt_conv_cuda.ipynb
@@ -143,7 +143,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/8d55b8f991fb704002f768367ce2d1a2/tvmc_python.ipynb b/docs/_downloads/8d55b8f991fb704002f768367ce2d1a2/tvmc_python.ipynb
index 91c9328f29..9fabfdf027 100644
--- a/docs/_downloads/8d55b8f991fb704002f768367ce2d1a2/tvmc_python.ipynb
+++ b/docs/_downloads/8d55b8f991fb704002f768367ce2d1a2/tvmc_python.ipynb
@@ -133,7 +133,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/910e6ecee4ecac8d8ca0baeb6d00689d/tune_relay_x86.ipynb b/docs/_downloads/910e6ecee4ecac8d8ca0baeb6d00689d/tune_relay_x86.ipynb
index 87fdb7820e..bd8099a8ce 100644
--- a/docs/_downloads/910e6ecee4ecac8d8ca0baeb6d00689d/tune_relay_x86.ipynb
+++ b/docs/_downloads/910e6ecee4ecac8d8ca0baeb6d00689d/tune_relay_x86.ipynb
@@ -107,7 +107,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/95395e118195f25266654dd8fbf487d4/deploy_classification.ipynb b/docs/_downloads/95395e118195f25266654dd8fbf487d4/deploy_classification.ipynb
index 6b5b7e11e0..37b79eac37 100644
--- a/docs/_downloads/95395e118195f25266654dd8fbf487d4/deploy_classification.ipynb
+++ b/docs/_downloads/95395e118195f25266654dd8fbf487d4/deploy_classification.ipynb
@@ -125,7 +125,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/9f81bc348ac4107d0670f512b8943a99/introduction.ipynb b/docs/_downloads/9f81bc348ac4107d0670f512b8943a99/introduction.ipynb
index 371418215b..598318216b 100644
--- a/docs/_downloads/9f81bc348ac4107d0670f512b8943a99/introduction.ipynb
+++ b/docs/_downloads/9f81bc348ac4107d0670f512b8943a99/introduction.ipynb
@@ -42,7 +42,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a1417396e306d987107a7a39376ec261/tuple_inputs.ipynb b/docs/_downloads/a1417396e306d987107a7a39376ec261/tuple_inputs.ipynb
index 460d8a2827..1167354310 100644
--- a/docs/_downloads/a1417396e306d987107a7a39376ec261/tuple_inputs.ipynb
+++ b/docs/_downloads/a1417396e306d987107a7a39376ec261/tuple_inputs.ipynb
@@ -114,7 +114,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a269cb38341b190be980a0bd3ea8a625/deploy_quantized.ipynb b/docs/_downloads/a269cb38341b190be980a0bd3ea8a625/deploy_quantized.ipynb
index 1dde4bd918..4e9b214cfe 100644
--- a/docs/_downloads/a269cb38341b190be980a0bd3ea8a625/deploy_quantized.ipynb
+++ b/docs/_downloads/a269cb38341b190be980a0bd3ea8a625/deploy_quantized.ipynb
@@ -136,7 +136,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a608d8b69371e9bc149dd89f6db2c38e/from_paddle.ipynb b/docs/_downloads/a608d8b69371e9bc149dd89f6db2c38e/from_paddle.ipynb
index 1f6a414ad7..aa47c3614d 100644
--- a/docs/_downloads/a608d8b69371e9bc149dd89f6db2c38e/from_paddle.ipynb
+++ b/docs/_downloads/a608d8b69371e9bc149dd89f6db2c38e/from_paddle.ipynb
@@ -154,7 +154,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a74627f44186b95116fe0ed6f77e3b99/micro_custom_ide.ipynb b/docs/_downloads/a74627f44186b95116fe0ed6f77e3b99/micro_custom_ide.ipynb
index c427c97583..efd1d9a072 100644
--- a/docs/_downloads/a74627f44186b95116fe0ed6f77e3b99/micro_custom_ide.ipynb
+++ b/docs/_downloads/a74627f44186b95116fe0ed6f77e3b99/micro_custom_ide.ipynb
@@ -153,7 +153,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a7aff5918e1b86809a5bd1da8bef7229/tedd.ipynb b/docs/_downloads/a7aff5918e1b86809a5bd1da8bef7229/tedd.ipynb
index 11ade6c0ad..4a562bf0ff 100644
--- a/docs/_downloads/a7aff5918e1b86809a5bd1da8bef7229/tedd.ipynb
+++ b/docs/_downloads/a7aff5918e1b86809a5bd1da8bef7229/tedd.ipynb
@@ -157,7 +157,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb b/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
index cfa4bead60..927b9e4ec7 100644
--- a/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+++ b/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
@@ -348,7 +348,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/a883b8474634054b6a79c17a288aa8ed/from_coreml.ipynb b/docs/_downloads/a883b8474634054b6a79c17a288aa8ed/from_coreml.ipynb
index c69c6eb14e..1694206210 100644
--- a/docs/_downloads/a883b8474634054b6a79c17a288aa8ed/from_coreml.ipynb
+++ b/docs/_downloads/a883b8474634054b6a79c17a288aa8ed/from_coreml.ipynb
@@ -154,7 +154,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/ad2a7f55d615d188ad664d56696815a6/tune_network_x86.ipynb b/docs/_downloads/ad2a7f55d615d188ad664d56696815a6/tune_network_x86.ipynb
index 8f1c80018b..8de93ef903 100644
--- a/docs/_downloads/ad2a7f55d615d188ad664d56696815a6/tune_network_x86.ipynb
+++ b/docs/_downloads/ad2a7f55d615d188ad664d56696815a6/tune_network_x86.ipynb
@@ -139,7 +139,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/af264436d049e3cd84803b67b6620b63/tune_network_cuda.ipynb b/docs/_downloads/af264436d049e3cd84803b67b6620b63/tune_network_cuda.ipynb
index 62ebdff2f4..c105d5ef5c 100644
--- a/docs/_downloads/af264436d049e3cd84803b67b6620b63/tune_network_cuda.ipynb
+++ b/docs/_downloads/af264436d049e3cd84803b67b6620b63/tune_network_cuda.ipynb
@@ -139,7 +139,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb b/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb
index 83aacc24c2..02f7951120 100644
--- a/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb
+++ b/docs/_downloads/b11795df0596a55e4982bf895d0c8c38/bring_your_own_datatypes.ipynb
@@ -341,7 +341,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/b1b0cbd807166348a0eabbad6bfbbdaf/tune_relay_vta.ipynb b/docs/_downloads/b1b0cbd807166348a0eabbad6bfbbdaf/tune_relay_vta.ipynb
index bf2ea78756..0add5b490b 100644
--- a/docs/_downloads/b1b0cbd807166348a0eabbad6bfbbdaf/tune_relay_vta.ipynb
+++ b/docs/_downloads/b1b0cbd807166348a0eabbad6bfbbdaf/tune_relay_vta.ipynb
@@ -178,7 +178,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/b3f997c945cc7de3e03a1e0c4c73fabd/convolution_opt.ipynb b/docs/_downloads/b3f997c945cc7de3e03a1e0c4c73fabd/convolution_opt.ipynb
index a091531e6a..5820abcb36 100644
--- a/docs/_downloads/b3f997c945cc7de3e03a1e0c4c73fabd/convolution_opt.ipynb
+++ b/docs/_downloads/b3f997c945cc7de3e03a1e0c4c73fabd/convolution_opt.ipynb
@@ -186,7 +186,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/b78f1a6e1b2c2fb073a791dc258a1d7d/schedule_primitives.ipynb b/docs/_downloads/b78f1a6e1b2c2fb073a791dc258a1d7d/schedule_primitives.ipynb
index 015bba396f..5345818b4b 100644
--- a/docs/_downloads/b78f1a6e1b2c2fb073a791dc258a1d7d/schedule_primitives.ipynb
+++ b/docs/_downloads/b78f1a6e1b2c2fb073a791dc258a1d7d/schedule_primitives.ipynb
@@ -276,7 +276,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb b/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb
index 4244b3e7d8..20d8bdba32 100644
--- a/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb
+++ b/docs/_downloads/b954238c1884e83b45d2ae543d824f03/using_relay_viz.ipynb
@@ -161,7 +161,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/b9e7311d8c56eb6e6aca08f0be35ff03/deploy_model_on_adreno.ipynb b/docs/_downloads/b9e7311d8c56eb6e6aca08f0be35ff03/deploy_model_on_adreno.ipynb
index e8ed3e5a58..32aeba06fb 100644
--- a/docs/_downloads/b9e7311d8c56eb6e6aca08f0be35ff03/deploy_model_on_adreno.ipynb
+++ b/docs/_downloads/b9e7311d8c56eb6e6aca08f0be35ff03/deploy_model_on_adreno.ipynb
@@ -283,7 +283,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/bc33c0d33026b287306b6ead1a50b04a/tune_relay_arm.ipynb b/docs/_downloads/bc33c0d33026b287306b6ead1a50b04a/tune_relay_arm.ipynb
index 0af5b66b78..49a1e32cba 100644
--- a/docs/_downloads/bc33c0d33026b287306b6ead1a50b04a/tune_relay_arm.ipynb
+++ b/docs/_downloads/bc33c0d33026b287306b6ead1a50b04a/tune_relay_arm.ipynb
@@ -160,7 +160,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/c00933f3fbcf90c4f584d54607b33805/micro_aot.ipynb b/docs/_downloads/c00933f3fbcf90c4f584d54607b33805/micro_aot.ipynb
index 7ffdcb81be..c017fb8a98 100644
--- a/docs/_downloads/c00933f3fbcf90c4f584d54607b33805/micro_aot.ipynb
+++ b/docs/_downloads/c00933f3fbcf90c4f584d54607b33805/micro_aot.ipynb
@@ -190,7 +190,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/c20f81a94729f461f33b52cc110fd9d6/deploy_prequantized.ipynb b/docs/_downloads/c20f81a94729f461f33b52cc110fd9d6/deploy_prequantized.ipynb
index 461aece101..4d55175f0d 100644
--- a/docs/_downloads/c20f81a94729f461f33b52cc110fd9d6/deploy_prequantized.ipynb
+++ b/docs/_downloads/c20f81a94729f461f33b52cc110fd9d6/deploy_prequantized.ipynb
@@ -279,7 +279,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/c82f632d47458e76d2af9821b6778e36/from_keras.ipynb b/docs/_downloads/c82f632d47458e76d2af9821b6778e36/from_keras.ipynb
index 8db7e39786..202abb187b 100644
--- a/docs/_downloads/c82f632d47458e76d2af9821b6778e36/from_keras.ipynb
+++ b/docs/_downloads/c82f632d47458e76d2af9821b6778e36/from_keras.ipynb
@@ -154,7 +154,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb b/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb
index f4996393ae..0ca6f59e8e 100644
--- a/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb
+++ b/docs/_downloads/c9bb7875c6ca5b2da162e177d3c9aac0/tensor_ir_blitz_course.ipynb
@@ -215,7 +215,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/cafefaac0e14b00fd7644da616cab35a/deploy_model_on_nano.ipynb b/docs/_downloads/cafefaac0e14b00fd7644da616cab35a/deploy_model_on_nano.ipynb
index 35fbb577d3..ddb5f36d74 100644
--- a/docs/_downloads/cafefaac0e14b00fd7644da616cab35a/deploy_model_on_nano.ipynb
+++ b/docs/_downloads/cafefaac0e14b00fd7644da616cab35a/deploy_model_on_nano.ipynb
@@ -193,7 +193,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
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diff --git a/docs/_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.py b/docs/_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.py
deleted file mode 100644
index af15a9337c..0000000000
--- a/docs/_downloads/cccb17d28e5e8b2e94ea8cd5ec59f6ed/deploy_ssd_gluoncv.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""
-Deploy Single Shot Multibox Detector(SSD) model
-===============================================
-**Author**: `Yao Wang <https://github.com/kevinthesun>`_
-`Leyuan Wang <https://github.com/Laurawly>`_
-
-This article is an introductory tutorial to deploy SSD models with TVM.
-We will use GluonCV pre-trained SSD model and convert it to Relay IR
-"""
-
-import tvm
-from tvm import te
-
-from matplotlib import pyplot as plt
-from tvm import relay
-from tvm.contrib import graph_executor
-from tvm.contrib.download import download_testdata
-from gluoncv import model_zoo, data, utils
-
-
-######################################################################
-# Preliminary and Set parameters
-# ------------------------------
-# .. note::
-#
-# We support compiling SSD on both CPUs and GPUs now.
-#
-# To get best inference performance on CPU, change
-# target argument according to your device and
-# follow the :ref:`tune_relay_x86` to tune x86 CPU and
-# :ref:`tune_relay_arm` for arm CPU.
-#
-# To get best inference performance on Intel graphics,
-# change target argument to :code:`opencl -device=intel_graphics`.
-# But when using Intel graphics on Mac, target needs to
-# be set to `opencl` only for the reason that Intel subgroup
-# extension is not supported on Mac.
-#
-# To get best inference performance on CUDA-based GPUs,
-# change the target argument to :code:`cuda`; and for
-# OPENCL-based GPUs, change target argument to
-# :code:`opencl` followed by device argument according
-# to your device.
-
-supported_model = [
- "ssd_512_resnet50_v1_voc",
- "ssd_512_resnet50_v1_coco",
- "ssd_512_resnet101_v2_voc",
- "ssd_512_mobilenet1.0_voc",
- "ssd_512_mobilenet1.0_coco",
- "ssd_300_vgg16_atrous_voc" "ssd_512_vgg16_atrous_coco",
-]
-
-model_name = supported_model[0]
-dshape = (1, 3, 512, 512)
-
-######################################################################
-# Download and pre-process demo image
-
-im_fname = download_testdata(
- "https://github.com/dmlc/web-data/blob/main/" + "gluoncv/detection/street_small.jpg?raw=true",
- "street_small.jpg",
- module="data",
-)
-x, img = data.transforms.presets.ssd.load_test(im_fname, short=512)
-
-######################################################################
-# Convert and compile model for CPU.
-
-block = model_zoo.get_model(model_name, pretrained=True)
-
-
-def build(target):
- mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
- with tvm.transform.PassContext(opt_level=3):
- lib = relay.build(mod, target, params=params)
- return lib
-
-
-######################################################################
-# Create TVM runtime and do inference
-# .. note::
-#
-# Use target = "cuda -libs" to enable thrust based sort, if you
-# enabled thrust during cmake by -DUSE_THRUST=ON.
-
-
-def run(lib, dev):
- # Build TVM runtime
- m = graph_executor.GraphModule(lib["default"](dev))
- tvm_input = tvm.nd.array(x.asnumpy(), device=dev)
- m.set_input("data", tvm_input)
- # execute
- m.run()
- # get outputs
- class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
- return class_IDs, scores, bounding_boxs
-
-
-for target in ["llvm", "cuda"]:
- dev = tvm.device(target, 0)
- if dev.exist:
- lib = build(target)
- class_IDs, scores, bounding_boxs = run(lib, dev)
-
-######################################################################
-# Display result
-
-ax = utils.viz.plot_bbox(
- img,
- bounding_boxs.numpy()[0],
- scores.numpy()[0],
- class_IDs.numpy()[0],
- class_names=block.classes,
-)
-plt.show()
diff --git a/docs/_downloads/d1434e80dd27eef6b1c9cbaa13f1197b/tune_relay_cuda.ipynb b/docs/_downloads/d1434e80dd27eef6b1c9cbaa13f1197b/tune_relay_cuda.ipynb
index e9bec39bce..09ab8cb0f1 100644
--- a/docs/_downloads/d1434e80dd27eef6b1c9cbaa13f1197b/tune_relay_cuda.ipynb
+++ b/docs/_downloads/d1434e80dd27eef6b1c9cbaa13f1197b/tune_relay_cuda.ipynb
@@ -164,7 +164,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb b/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb
index 19839c25d1..287f951e12 100644
--- a/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb
+++ b/docs/_downloads/d58ec306b89044968adefb49e6552378/low_level_custom_pass.ipynb
@@ -132,7 +132,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/d92aacfae35477bed0f7f60aa8d2714e/deploy_ssd_gluoncv.ipynb b/docs/_downloads/d92aacfae35477bed0f7f60aa8d2714e/deploy_ssd_gluoncv.ipynb
deleted file mode 100644
index 11830c70c0..0000000000
--- a/docs/_downloads/d92aacfae35477bed0f7f60aa8d2714e/deploy_ssd_gluoncv.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%%shell\n# Installs the latest dev build of TVM from PyPI. If you wish to build\n# from source, see https://tvm.apache.org/docs/install/from_source.html\npip install apache-tvm --pre"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n# Deploy Single Shot Multibox Detector(SSD) model\n**Author**: [Yao Wang](https://github.com/kevinthesun)\n[Leyuan Wang](https://github.com/Laurawly)\n\nThis article is an introductory tutorial to deploy SSD models with TVM.\nWe will use GluonCV pre-trained SSD model and convert it to Relay IR\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "import tvm\nfrom tvm import te\n\nfrom matplotlib import pyplot as plt\nfrom tvm import relay\nfrom tvm.contrib import graph_executor\nfrom tvm.contrib.download import download_testdata\nfrom gluoncv import model_zoo, data, utils"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Preliminary and Set parameters\n<div class=\"alert alert-info\"><h4>Note</h4><p>We support compiling SSD on both CPUs and GPUs now.\n\n To get best inference performance on CPU, change\n target argument according to your device and\n follow the `tune_relay_x86` to tune x86 CPU and\n `tune_relay_arm` for arm CPU.\n\n To get best inference performance on Intel graphics,\n change target argument to :code:`opencl -device=intel_graphics`.\n But when using Intel graphics on [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "supported_model = [\n \"ssd_512_resnet50_v1_voc\",\n \"ssd_512_resnet50_v1_coco\",\n \"ssd_512_resnet101_v2_voc\",\n \"ssd_512_mobilenet1.0_voc\",\n \"ssd_512_mobilenet1.0_coco\",\n \"ssd_300_vgg16_atrous_voc\" \"ssd_512_vgg16_atrous_coco\",\n]\n\nmodel_name = supported_model[0]\ndshape = (1, 3, 512, 512)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download and pre-process demo image\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "im_fname = download_testdata(\n \"https://github.com/dmlc/web-data/blob/main/\" + \"gluoncv/detection/street_small.jpg?raw=true\",\n \"street_small.jpg\",\n module=\"data\",\n)\nx, img = data.transforms.presets.ssd.load_test(im_fname, short=512)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Convert and compile model for CPU.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "block = model_zoo.get_model(model_name, pretrained=True)\n\n\ndef build(target):\n mod, params = relay.frontend.from_mxnet(block, {\"data\": dshape})\n with tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target, params=params)\n return lib"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create TVM runtime and do inference\n<div class=\"alert alert-info\"><h4>Note</h4><p>Use target = \"cuda -libs\" to enable thrust based sort, if you\n enabled thrust during cmake by -DUSE_THRUST=ON.</p></div>\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "def run(lib, dev):\n # Build TVM runtime\n m = graph_executor.GraphModule(lib[\"default\"](dev))\n tvm_input = tvm.nd.array(x.asnumpy(), device=dev)\n m.set_input(\"data\", tvm_input)\n # execute\n m.run()\n # get outputs\n class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)\n return class_IDs, scores, bounding_boxs\n\n\nfor target in [\"llvm\", \"cuda\"]:\n dev = tvm.device(target, 0)\n if dev.exist:\n lib [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Display result\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "ax = utils.viz.plot_bbox(\n img,\n bounding_boxs.numpy()[0],\n scores.numpy()[0],\n class_IDs.numpy()[0],\n class_names=block.classes,\n)\nplt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py b/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py
index 16a87fb0f1..a4bb5d58cd 100644
--- a/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py
+++ b/docs/_downloads/dabb6b43ea9ef9d7bd1a3912001deace/build_gcn.py
@@ -28,8 +28,8 @@ We directly load the dataset from DGL library to do the apples to apples compari
.. code-block:: bash
%%shell
- pip install torch==1.9.0
- pip install dgl==v0.7.2 -f https://data.dgl.ai/wheels/repo.html
+ pip install torch==2.0.0
+ pip install dgl==v1.0.0
Please refer to DGL doc for installation at
https://docs.dgl.ai/install/index.html.
@@ -82,23 +82,12 @@ from dgl.data import load_data
from collections import namedtuple
-def load_dataset(dataset="cora"):
- args = namedtuple("args", ["dataset"])
- data = load_data(args(dataset))
-
- # Remove self-loops to avoid duplicate passing of a node's feature to itself
- g = data.graph
- g.remove_edges_from(nx.selfloop_edges(g))
- g.add_edges_from(zip(g.nodes, g.nodes))
-
- return g, data
-
-
-def evaluate(data, logits):
- test_mask = data.test_mask # the test set which isn't included in the training phase
+def evaluate(g, logits):
+ label = g.ndata["label"]
+ test_mask = g.ndata["test_mask"]
pred = logits.argmax(axis=1)
- acc = ((pred == data.labels) * test_mask).sum() / test_mask.sum()
+ acc = (torch.Tensor(pred[test_mask]) == label[test_mask]).float().mean()
return acc
@@ -109,9 +98,6 @@ def evaluate(data, logits):
"""
Parameters
----------
-dataset: str
- Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed'].
-
num_layer: int
number of hidden layers
@@ -125,13 +111,13 @@ num_classes: int
dimension of model output (Number of classes)
"""
-dataset = "cora"
-g, data = load_dataset(dataset)
-
+dataset = dgl.data.CoraGraphDataset()
+dgl_g = dataset[0]
num_layers = 1
num_hidden = 16
-infeat_dim = data.features.shape[1]
-num_classes = data.num_labels
+features = dgl_g.ndata["feat"]
+infeat_dim = features.shape[1]
+num_classes = dataset.num_classes
######################################################################
# Set up the DGL-PyTorch model and get the golden results
@@ -139,16 +125,14 @@ num_classes = data.num_labels
#
# The weights are trained with https://github.com/dmlc/dgl/blob/master/examples/pytorch/gcn/train.py
from tvm.contrib.download import download_testdata
-from dgl import DGLGraph
-features = torch.FloatTensor(data.features)
-dgl_g = DGLGraph(g)
+features = torch.FloatTensor(features)
torch_model = GCN(dgl_g, infeat_dim, num_hidden, num_classes, num_layers, F.relu)
# Download the pretrained weights
-model_url = "https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_%s.torch" % (dataset)
-model_path = download_testdata(model_url, "gcn_%s.pickle" % (dataset), module="gcn_model")
+model_url = "https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_cora.torch"
+model_path = download_testdata(model_url, "gcn_cora.pickle", module="gcn_model")
# Load the weights into the model
torch_model.load_state_dict(torch.load(model_path))
@@ -162,7 +146,7 @@ with torch.no_grad():
logits_torch = torch_model(features)
print("Print the first five outputs from DGL-PyTorch execution\n", logits_torch[:5])
-acc = evaluate(data, logits_torch.numpy())
+acc = evaluate(dgl_g, logits_torch.numpy())
print("Test accuracy of DGL results: {:.2%}".format(acc))
@@ -246,27 +230,26 @@ import numpy as np
import networkx as nx
-def prepare_params(g, data):
+def prepare_params(g):
params = {}
- params["infeats"] = data.features.numpy().astype(
- "float32"
- ) # Only support float32 as feature for now
+ params["infeats"] = g.ndata["feat"].numpy().astype("float32")
# Generate adjacency matrix
- adjacency = nx.to_scipy_sparse_matrix(g)
+ nx_graph = dgl.to_networkx(g)
+ adjacency = nx.to_scipy_sparse_array(nx_graph)
params["g_data"] = adjacency.data.astype("float32")
params["indices"] = adjacency.indices.astype("int32")
params["indptr"] = adjacency.indptr.astype("int32")
# Normalization w.r.t. node degrees
- degs = [g.in_degree[i] for i in range(g.number_of_nodes())]
+ degs = [g.in_degrees(i) for i in range(g.number_of_nodes())]
params["norm"] = np.power(degs, -0.5).astype("float32")
params["norm"] = params["norm"].reshape((params["norm"].shape[0], 1))
return params
-params = prepare_params(g, data)
+params = prepare_params(dgl_g)
# Check shape of features and the validity of adjacency matrix
assert len(params["infeats"].shape) == 2
@@ -280,7 +263,7 @@ assert params["infeats"].shape[0] == params["indptr"].shape[0] - 1
# -------------------
# Define input features, norms, adjacency matrix in Relay
-infeats = relay.var("infeats", shape=data.features.shape)
+infeats = relay.var("infeats", shape=features.shape)
norm = relay.Constant(tvm.nd.array(params["norm"]))
g_data = relay.Constant(tvm.nd.array(params["g_data"]))
indices = relay.Constant(tvm.nd.array(params["indices"]))
@@ -352,10 +335,7 @@ m.run()
logits_tvm = m.get_output(0).numpy()
print("Print the first five outputs from TVM execution\n", logits_tvm[:5])
-labels = data.labels
-test_mask = data.test_mask
-
-acc = evaluate(data, logits_tvm)
+acc = evaluate(dgl_g, logits_tvm)
print("Test accuracy of TVM results: {:.2%}".format(acc))
import tvm.testing
diff --git a/docs/_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py b/docs/_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py
index ea03869c8c..09dcd020d3 100644
--- a/docs/_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py
+++ b/docs/_downloads/e3e540f3b477c0c52d8eb73e674e8ffd/tune_conv2d_layer_cuda.py
@@ -119,7 +119,9 @@ tune_option = auto_scheduler.TuningOptions(
# file and apply it.
# Run auto-tuning (search)
-task.tune(tune_option)
+# We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+# task.tune(tune_option)
# Apply the best schedule
sch, args = task.apply_best(log_file)
@@ -209,4 +211,6 @@ def resume_search(task, log_file):
del measure_ctx
-resume_search(task, log_file)
+# We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+# resume_search(task, log_file)
diff --git a/docs/_downloads/edc9d28c4fbc249e2e7b78002af63b84/using_external_lib.ipynb b/docs/_downloads/edc9d28c4fbc249e2e7b78002af63b84/using_external_lib.ipynb
index 64e90366af..932ba3aa13 100644
--- a/docs/_downloads/edc9d28c4fbc249e2e7b78002af63b84/using_external_lib.ipynb
+++ b/docs/_downloads/edc9d28c4fbc249e2e7b78002af63b84/using_external_lib.ipynb
@@ -139,7 +139,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/eed2658f15243bab719b2de7769fa45a/deploy_model_on_android.ipynb b/docs/_downloads/eed2658f15243bab719b2de7769fa45a/deploy_model_on_android.ipynb
index e027344900..32f7ce18b4 100644
--- a/docs/_downloads/eed2658f15243bab719b2de7769fa45a/deploy_model_on_android.ipynb
+++ b/docs/_downloads/eed2658f15243bab719b2de7769fa45a/deploy_model_on_android.ipynb
@@ -182,7 +182,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/efe0b02e219b28e0bd85fbdda35ba8ac/tvmc_command_line_driver.ipynb b/docs/_downloads/efe0b02e219b28e0bd85fbdda35ba8ac/tvmc_command_line_driver.ipynb
index 011f5a9903..7b68da31b1 100644
--- a/docs/_downloads/efe0b02e219b28e0bd85fbdda35ba8ac/tvmc_command_line_driver.ipynb
+++ b/docs/_downloads/efe0b02e219b28e0bd85fbdda35ba8ac/tvmc_command_line_driver.ipynb
@@ -133,7 +133,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/f289ca2466fcf79c024068c1f8642bd0/cross_compilation_and_rpc.ipynb b/docs/_downloads/f289ca2466fcf79c024068c1f8642bd0/cross_compilation_and_rpc.ipynb
index 892781ed49..beab4492cf 100644
--- a/docs/_downloads/f289ca2466fcf79c024068c1f8642bd0/cross_compilation_and_rpc.ipynb
+++ b/docs/_downloads/f289ca2466fcf79c024068c1f8642bd0/cross_compilation_and_rpc.ipynb
@@ -171,7 +171,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/f407f66fb8174d0d4ec37407af1128d6/using_pipeline_executor.ipynb b/docs/_downloads/f407f66fb8174d0d4ec37407af1128d6/using_pipeline_executor.ipynb
index b278b3e865..3e0f3c0455 100644
--- a/docs/_downloads/f407f66fb8174d0d4ec37407af1128d6/using_pipeline_executor.ipynb
+++ b/docs/_downloads/f407f66fb8174d0d4ec37407af1128d6/using_pipeline_executor.ipynb
@@ -370,7 +370,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb b/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb
index 7ef2f98624..c21b9931ac 100644
--- a/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb
+++ b/docs/_downloads/f6ff0fbc61d45d2cc0f53ebbf11a5fb5/use_pass_instrument.ipynb
@@ -334,7 +334,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb b/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb
index 775cac75bf..31116d68d4 100644
--- a/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb
+++ b/docs/_downloads/f83ba3df2d52f9b54cf141114359481a/micro_autotune.ipynb
@@ -226,7 +226,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_downloads/f97d815b408ef3f4d6bcb3e073c2d4dd/from_darknet.ipynb b/docs/_downloads/f97d815b408ef3f4d6bcb3e073c2d4dd/from_darknet.ipynb
index 3f45a07515..0ff002950a 100644
--- a/docs/_downloads/f97d815b408ef3f4d6bcb3e073c2d4dd/from_darknet.ipynb
+++ b/docs/_downloads/f97d815b408ef3f4d6bcb3e073c2d4dd/from_darknet.ipynb
@@ -147,7 +147,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.8.16"
}
},
"nbformat": 4,
diff --git a/docs/_images/sphx_glr_deploy_classification_001.png b/docs/_images/sphx_glr_deploy_classification_001.png
index a015bbcb3a..6ee139f54d 100644
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diff --git a/docs/_images/sphx_glr_deploy_detection_001.png b/docs/_images/sphx_glr_deploy_detection_001.png
index e3067243fa..153be4cfd8 100644
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diff --git a/docs/_images/sphx_glr_deploy_model_on_adreno_001.png b/docs/_images/sphx_glr_deploy_model_on_adreno_001.png
index a015bbcb3a..6ee139f54d 100644
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diff --git a/docs/_images/sphx_glr_deploy_ssd_gluoncv_001.png b/docs/_images/sphx_glr_deploy_ssd_gluoncv_001.png
deleted file mode 100644
index 3b47c81250..0000000000
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diff --git a/docs/_images/sphx_glr_deploy_ssd_gluoncv_thumb.png b/docs/_images/sphx_glr_deploy_ssd_gluoncv_thumb.png
deleted file mode 100644
index 6b0a7dee84..0000000000
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diff --git a/docs/_images/sphx_glr_from_darknet_001.png b/docs/_images/sphx_glr_from_darknet_001.png
index 2017c4cdf6..829e099034 100644
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diff --git a/docs/_images/sphx_glr_from_keras_001.png b/docs/_images/sphx_glr_from_keras_001.png
index a015bbcb3a..6ee139f54d 100644
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diff --git a/docs/_images/sphx_glr_from_mxnet_001.png b/docs/_images/sphx_glr_from_mxnet_001.png
index a015bbcb3a..6ee139f54d 100644
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diff --git a/docs/_images/sphx_glr_from_onnx_001.png b/docs/_images/sphx_glr_from_onnx_001.png
index cb6ddf00ab..0dae0e237a 100644
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diff --git a/docs/_images/sphx_glr_from_tflite_001.png b/docs/_images/sphx_glr_from_tflite_001.png
index a015bbcb3a..6ee139f54d 100644
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diff --git a/docs/_images/sphx_glr_micro_train_001.png b/docs/_images/sphx_glr_micro_train_001.png
index 60e9d514ac..e75191eb4e 100644
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diff --git a/docs/_images/sphx_glr_micro_train_thumb.png b/docs/_images/sphx_glr_micro_train_thumb.png
index 537d31c680..2c5a5b88a4 100644
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diff --git a/docs/_sources/how_to/compile_models/from_darknet.rst.txt b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
index b0845f29b4..1f6d6261fd 100644
--- a/docs/_sources/how_to/compile_models/from_darknet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_darknet.rst.txt
@@ -318,7 +318,7 @@ The process is no different from other examples.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 25.093 seconds)
+ **Total running time of the script:** ( 1 minutes 32.382 seconds)
.. _sphx_glr_download_how_to_compile_models_from_darknet.py:
diff --git a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
index 21d358c3ed..29fe4faabd 100644
--- a/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_mxnet.rst.txt
@@ -116,7 +116,7 @@ In this section, we download a pretrained imagenet model and classify an image.
.. code-block:: none
- Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zipd3cea108-cddf-4243-bc9e-ec014e8765ff from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
+ Downloading /workspace/.mxnet/models/resnet18_v1-a0666292.zip05e3c05a-4566-484a-891f-0dabcfcd3b88 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...
x (1, 3, 224, 224)
diff --git a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
index ad5f8bec01..7a5c40cd72 100644
--- a/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_oneflow.rst.txt
@@ -70,26 +70,26 @@ Currently, TVM supports OneFlow 0.7.0. Other versions may be unstable.
.. code-block:: none
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/oneflow/framework/dtype.py:49: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/oneflow/framework/dtype.py:49: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
oneflow.bool: np.bool,
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional_pil.py:193: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional_pil.py:193: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
def resize(img, size, interpolation=Image.BILINEAR):
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional.py:65: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional.py:65: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
Image.NEAREST: "nearest",
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional.py:66: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional.py:66: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
Image.BILINEAR: "bilinear",
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional.py:67: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional.py:67: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
Image.BICUBIC: "bicubic",
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional.py:68: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional.py:68: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
Image.BOX: "box",
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional.py:69: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional.py:69: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
Image.HAMMING: "hamming",
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/transforms/functional.py:70: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/transforms/functional.py:70: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
Image.LANCZOS: "lanczos",
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/data/auto_augment.py:28: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/data/auto_augment.py:28: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/flowvision/data/auto_augment.py:28: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/flowvision/data/auto_augment.py:28: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
@@ -121,7 +121,7 @@ Load a pretrained OneFlow model and save model
.. code-block:: none
Downloading: "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" to /workspace/.oneflow/flowvision_cache/resnet18.zip
-
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15%|#5 | 6.33M/41.5M [00:00<00:00, 51.8MB/s]
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71%|####### | 29.3M/41.5M [00:00<00:00, 51.2MB/s]
83%|########2 | 34.3M/41.5M [00:00<00:00, 45.9MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 51.4MB/s]
+
0%| | 0.00/41.5M [00:00<?, ?B/s]
19%|#9 | 7.99M/41.5M [00:00<00:00, 45.1MB/s]
39%|###8 | 16.0M/41.5M [00:00<00:00, 56.5MB/s]
58%|#####7 | 24.0M/41.5M [00:00<00:00, 55.1MB/s]
77%|#######7 | 32.0M/41.5M [00:00<00:00, 56.0MB/s]
96%|#########6| 40.0M/41.5M [00:00<00:00, 58.6MB/s]
100%|##########| 41.5M/41.5M [00:00<00:00, 58.2MB/s]
diff --git a/docs/_sources/how_to/compile_models/from_paddle.rst.txt b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
index 8df75c9e33..4799b816c7 100644
--- a/docs/_sources/how_to/compile_models/from_paddle.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_paddle.rst.txt
@@ -207,6 +207,11 @@ Look up prediction top 1 index in 1000 class synset.
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 1.267 seconds)
+
+
.. _sphx_glr_download_how_to_compile_models_from_paddle.py:
.. only:: html
diff --git a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
index d0ad51d1b1..fbb8a09e92 100644
--- a/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_pytorch.rst.txt
@@ -96,12 +96,12 @@ Load a pretrained PyTorch model
.. code-block:: none
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
- f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+ warnings.warn(
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-
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+
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diff --git a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
index cff2586d0f..38e9c39ad6 100644
--- a/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
+++ b/docs/_sources/how_to/compile_models/from_tensorflow.rst.txt
@@ -242,8 +242,8 @@ Results:
.. code-block:: none
- /workspace/python/tvm/relay/frontend/tensorflow.py:539: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
- "will be used for operator %s." % node.name
+ /workspace/python/tvm/relay/frontend/tensorflow.py:537: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
+ warnings.warn(
/workspace/python/tvm/relay/frontend/tensorflow_ops.py:1012: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
Tensorflow protobuf imported to relay frontend.
@@ -430,7 +430,7 @@ Run the corresponding model on tensorflow
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 28.638 seconds)
+ **Total running time of the script:** ( 1 minutes 31.078 seconds)
.. _sphx_glr_download_how_to_compile_models_from_tensorflow.py:
diff --git a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
index 6c7dbd6a71..c34c29b079 100644
--- a/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/compile_models/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**06:59.625** total execution time for **how_to_compile_models** files:
+**07:02.130** total execution time for **how_to_compile_models** files:
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:28.638 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:32.382 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_darknet.py` (``from_darknet.py``) | 01:25.093 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tensorflow.py` (``from_tensorflow.py``) | 01:31.078 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 00:59.109 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_paddle.py` (``from_paddle.py``) | 01:01.267 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:39.035 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_oneflow.py` (``from_oneflow.py``) | 00:39.869 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:33.502 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_coreml.py` (``from_coreml.py``) | 00:37.528 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:30.977 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_mxnet.py` (``from_mxnet.py``) | 00:32.938 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:28.900 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:27.963 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_pytorch.py` (``from_pytorch.py``) | 00:26.590 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.596 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_keras.py` (``from_keras.py``) | 00:25.024 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_tflite.py` (``from_tflite.py``) | 00:10.681 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.757 | 0.0 MB |
+| :ref:`sphx_glr_how_to_compile_models_from_onnx.py` (``from_onnx.py``) | 00:02.828 | 0.0 MB |
+-----------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
index c8c5020c83..1193a8ca23 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno.rst.txt
@@ -221,9 +221,9 @@ Get resnet18 from torchvision models
.. code-block:: none
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
- f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+ warnings.warn(
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
@@ -304,7 +304,7 @@ Input name can be arbitrary
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
return LooseVersion(torch_ver) > ver
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
other = LooseVersion(other)
@@ -658,8 +658,8 @@ well as provides information about the model's performance
.. code-block:: none
- /workspace/python/tvm/runtime/ndarray.py:200: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
- DeprecationWarning,
+ /workspace/python/tvm/runtime/ndarray.py:197: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead.
+ warnings.warn(
Top-1 id: 281, class name: tabby, tabby cat
Top5 predictions:
@@ -673,7 +673,7 @@ well as provides information about the model's performance
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 3294.5894 3294.3619 3301.5834 3291.9183 2.5845
+ 4235.6601 4232.6672 4261.8405 4227.6768 10.2050
@@ -682,7 +682,7 @@ well as provides information about the model's performance
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 4.632 seconds)
+ **Total running time of the script:** ( 1 minutes 20.350 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_model_on_adreno.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
index 874f81125d..54980f3bb4 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_adreno_tvmc.rst.txt
@@ -127,7 +127,7 @@ Make a Keras Resnet50 Model
.. code-block:: none
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
-
8192/102967424 [..............................] - ETA: 0s
8380416/102967424 [=>............................] - ETA: 1s
16769024/102967424 [===>..........................] - ETA: 1s
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75489280/102967424 [====================>.........] -
ETA: 0s
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98910208/102967424 [===========================>..] - ETA: 0s
102850560/102967424 [============================>.] - ETA: 0s
102967424/102967424 [==============================] - 2s 0us/step
+
8192/102967424 [..............................] - ETA: 0s
8380416/102967424 [=>............................] - ETA: 0s
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80904192/102967424 [======================>.......] - ETA: 0s
83877888/102967424 [=======================>......] - ETA: 0s
92012544/102967424 [=========================>....] -
ETA: 0s
92266496/102967424 [=========================>....] - ETA: 0s
100728832/102967424 [============================>.] - ETA: 0s
102967424/102967424 [==============================] - 1s 0us/step
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
index 5554a2ec69..4f05a36738 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_android.rst.txt
@@ -437,7 +437,7 @@ Execute on TVM
Evaluate inference time cost...
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 15.2404 15.0316 16.0793 14.7557 0.4819
+ 16.1580 16.0372 17.0183 15.4291 0.4671
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_nano.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_nano.rst.txt
index 58bc41b021..253839cade 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_nano.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_nano.rst.txt
@@ -305,8 +305,8 @@ if you want to run this tutorial with a real device.
.. code-block:: none
- /workspace/python/tvm/relay/build_module.py:348: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
- DeprecationWarning,
+ /workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+ warnings.warn(
diff --git a/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt b/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt
index 3c32a114e4..4fb2456376 100644
--- a/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_model_on_rasp.rst.txt
@@ -296,8 +296,8 @@ to run this tutorial with a real device.
.. code-block:: none
- /workspace/python/tvm/relay/build_module.py:348: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
- DeprecationWarning,
+ /workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+ warnings.warn(
diff --git a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
index f0dd9965d1..cf0e8fcda0 100644
--- a/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_object_detection_pytorch.rst.txt
@@ -125,29 +125,25 @@ Load pre-trained maskrcnn from torchvision and do tracing
.. code-block:: none
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
- f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+ warnings.warn(
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth
-
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- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/nn/functional.py:3897: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
- for i in range(dim)
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/anchor_utils.py:124: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
- for g in grid_sizes
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/rpn.py:99: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
- A = Ax4 // 4
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/rpn.py:100: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
- C = AxC // A
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+
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+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+ (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/ops/boxes.py:159: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:159: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/__init__.py:833: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/__init__.py:1209: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert condition, message
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/transform.py:300: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
- for s, s_orig in zip(new_size, original_size)
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/detection/roi_heads.py:389: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/transform.py:298: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+ torch.tensor(s, dtype=torch.float32, device=boxes.device)
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/transform.py:299: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+ / torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py:389: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
return torch.tensor(M + 2 * padding).to(torch.float32) / torch.tensor(M).to(torch.float32)
@@ -203,10 +199,10 @@ Import the graph to Relay
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
return LooseVersion(torch_ver) > ver
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
other = LooseVersion(other)
- /workspace/python/tvm/relay/build_module.py:348: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
- DeprecationWarning,
+ /workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+ warnings.warn(
/workspace/python/tvm/relay/frontend/pytorch.py:451: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
inputs[3], lambda ret: ret.astype(np.int).item(0)
@@ -299,7 +295,7 @@ Get boxes with score larger than 0.9
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 39.216 seconds)
+ **Total running time of the script:** ( 3 minutes 46.821 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_object_detection_pytorch.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
index d19e5d64c9..e400d7cbd7 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized.rst.txt
@@ -222,12 +222,12 @@ training. Other models require a full post training calibration.
.. code-block:: none
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
- f"The parameter '{pretrained_param}' is deprecated since 0.13 and will be removed in 0.15, "
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+ warnings.warn(
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
-
0%| | 0.00/13.6M [00:00<?, ?B/s]
51%|##### | 6.88M/13.6M [00:00<00:00, 72.2MB/s]
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+
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@@ -258,10 +258,10 @@ on the PyTorch website to learn about quantization and jit.
.. code-block:: none
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/ao/quantization/observer.py:178: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.
- reduce_range will be deprecated in a future release of PyTorch."
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/torch/ao/quantization/observer.py:1137: UserWarning: must run observer before calling calculate_qparams. Returning default scale and zero point
- Returning default scale and zero point "
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/observer.py:214: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch.
+ warnings.warn(
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/observer.py:1209: UserWarning: must run observer before calling calculate_qparams. Returning default scale and zero point
+ warnings.warn(
@@ -299,7 +299,7 @@ qnn.quantize, qnn.dequantize, qnn.requantize, and qnn.conv2d etc.
/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
return LooseVersion(torch_ver) > ver
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
+ /venv/apache-tvm-py3.8/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
other = LooseVersion(other)
@@ -354,7 +354,7 @@ We should see identical labels printed.
.. code-block:: none
- PyTorch top3 labels: ['tiger cat', 'Egyptian cat', 'tabby, tabby cat']
+ PyTorch top3 labels: ['tiger cat', 'Egyptian cat', 'lynx, catamount']
TVM top3 labels: ['tiger cat', 'Egyptian cat', 'tabby, tabby cat']
@@ -380,7 +380,7 @@ output values are identical out of 1000 outputs from mobilenet v2.
.. code-block:: none
- 154 in 1000 raw floating outputs identical.
+ 122 in 1000 raw floating outputs identical.
@@ -409,7 +409,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 88.5353 88.4615 91.7388 88.2718 0.3668
+ 88.9716 88.8853 91.3742 88.6379 0.3270
@@ -458,7 +458,7 @@ TODO
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 18.960 seconds)
+ **Total running time of the script:** ( 1 minutes 28.103 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
index 752ea42beb..aaaf6aab7e 100644
--- a/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_prequantized_tflite.rst.txt
@@ -423,7 +423,7 @@ Here we give an example of how to measure performance of TVM compiled models.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 118.2995 118.2231 121.3082 117.7129 0.4078
+ 112.0903 111.5069 152.7342 111.0612 4.1639
@@ -458,11 +458,6 @@ Here we give an example of how to measure performance of TVM compiled models.
network for ARM CPU <tune_relay_arm>`.
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 2 minutes 32.486 seconds)
-
-
.. _sphx_glr_download_how_to_deploy_models_deploy_prequantized_tflite.py:
.. only:: html
diff --git a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
index 493be95113..f61d25e388 100644
--- a/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
+++ b/docs/_sources/how_to/deploy_models/deploy_quantized.rst.txt
@@ -248,8 +248,8 @@ We create a Relay VM to build and execute the model.
.. code-block:: none
- /workspace/python/tvm/relay/build_module.py:348: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
- DeprecationWarning,
+ /workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
+ warnings.warn(
@@ -257,7 +257,7 @@ We create a Relay VM to build and execute the model.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 50.023 seconds)
+ **Total running time of the script:** ( 2 minutes 2.477 seconds)
.. _sphx_glr_download_how_to_deploy_models_deploy_quantized.py:
diff --git a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt b/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
deleted file mode 100644
index 8622f38e5b..0000000000
--- a/docs/_sources/how_to/deploy_models/deploy_ssd_gluoncv.rst.txt
+++ /dev/null
@@ -1,272 +0,0 @@
-
-.. DO NOT EDIT. THIS FILE WAS AUTOMATICALLY GENERATED BY
-.. TVM'S MONKEY-PATCHED VERSION OF SPHINX-GALLERY. TO MAKE
-.. CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "how_to/deploy_models/deploy_ssd_gluoncv.py"
-
-.. only:: html
-
- .. note::
- :class: sphx-glr-download-link-note
-
- This tutorial can be used interactively with Google Colab! You can also click
- :ref:`here <sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.py>` to run the Jupyter notebook locally.
-
- .. image:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg
- :align: center
- :target: https://colab.research.google.com/github/apache/tvm-site/blob/asf-site/docs/_downloads/d92aacfae35477bed0f7f60aa8d2714e/deploy_ssd_gluoncv.ipynb
- :width: 300px
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py:
-
-
-Deploy Single Shot Multibox Detector(SSD) model
-===============================================
-**Author**: `Yao Wang <https://github.com/kevinthesun>`_
-`Leyuan Wang <https://github.com/Laurawly>`_
-
-This article is an introductory tutorial to deploy SSD models with TVM.
-We will use GluonCV pre-trained SSD model and convert it to Relay IR
-
-.. GENERATED FROM PYTHON SOURCE LINES 26-37
-
-.. code-block:: default
-
-
- import tvm
- from tvm import te
-
- from matplotlib import pyplot as plt
- from tvm import relay
- from tvm.contrib import graph_executor
- from tvm.contrib.download import download_testdata
- from gluoncv import model_zoo, data, utils
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/gluoncv/check.py:8: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
- if LooseVersion(mx.__version__) < LooseVersion(mx_version) or \
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/gluoncv/check.py:9: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
- LooseVersion(mx.__version__) >= LooseVersion(max_mx_version):
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/gluoncv/check.py:30: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
- if LooseVersion(torch.__version__) < LooseVersion(torch_version) or \
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/gluoncv/check.py:31: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
- LooseVersion(torch.__version__) >= LooseVersion(max_torch_version):
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/gluoncv/__init__.py:40: UserWarning: Both `mxnet==1.6.0` and `torch==1.12.0+cpu` are installed. You might encounter increased GPU memory footprint if both framework are used at the same time.
- warnings.warn(f'Both `mxnet=={mx.__version__}` and `torch=={torch.__version__}` are installed. '
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 38-60
-
-Preliminary and Set parameters
-------------------------------
-.. note::
-
- We support compiling SSD on both CPUs and GPUs now.
-
- To get best inference performance on CPU, change
- target argument according to your device and
- follow the :ref:`tune_relay_x86` to tune x86 CPU and
- :ref:`tune_relay_arm` for arm CPU.
-
- To get best inference performance on Intel graphics,
- change target argument to :code:`opencl -device=intel_graphics`.
- But when using Intel graphics on Mac, target needs to
- be set to `opencl` only for the reason that Intel subgroup
- extension is not supported on Mac.
-
- To get best inference performance on CUDA-based GPUs,
- change the target argument to :code:`cuda`; and for
- OPENCL-based GPUs, change target argument to
- :code:`opencl` followed by device argument according
- to your device.
-
-.. GENERATED FROM PYTHON SOURCE LINES 60-73
-
-.. code-block:: default
-
-
- supported_model = [
- "ssd_512_resnet50_v1_voc",
- "ssd_512_resnet50_v1_coco",
- "ssd_512_resnet101_v2_voc",
- "ssd_512_mobilenet1.0_voc",
- "ssd_512_mobilenet1.0_coco",
- "ssd_300_vgg16_atrous_voc" "ssd_512_vgg16_atrous_coco",
- ]
-
- model_name = supported_model[0]
- dshape = (1, 3, 512, 512)
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 74-75
-
-Download and pre-process demo image
-
-.. GENERATED FROM PYTHON SOURCE LINES 75-83
-
-.. code-block:: default
-
-
- im_fname = download_testdata(
- "https://github.com/dmlc/web-data/blob/main/" + "gluoncv/detection/street_small.jpg?raw=true",
- "street_small.jpg",
- module="data",
- )
- x, img = data.transforms.presets.ssd.load_test(im_fname, short=512)
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 84-85
-
-Convert and compile model for CPU.
-
-.. GENERATED FROM PYTHON SOURCE LINES 85-96
-
-.. code-block:: default
-
-
- block = model_zoo.get_model(model_name, pretrained=True)
-
-
- def build(target):
- mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
- with tvm.transform.PassContext(opt_level=3):
- lib = relay.build(mod, target, params=params)
- return lib
-
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- .. code-block:: none
-
- /venv/apache-tvm-py3.7/lib/python3.7/site-packages/mxnet/gluon/block.py:1389: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
- data: None
- input_sym_arg_type = in_param.infer_type()[0]
- Downloading /workspace/.mxnet/models/ssd_512_resnet50_v1_voc-9c8b225a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_voc-9c8b225a.zip...
-
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-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 97-102
-
-Create TVM runtime and do inference
-.. note::
-
- Use target = "cuda -libs" to enable thrust based sort, if you
- enabled thrust during cmake by -DUSE_THRUST=ON.
-
-.. GENERATED FROM PYTHON SOURCE LINES 102-122
-
-.. code-block:: default
-
-
-
- def run(lib, dev):
- # Build TVM runtime
- m = graph_executor.GraphModule(lib["default"](dev))
- tvm_input = tvm.nd.array(x.asnumpy(), device=dev)
- m.set_input("data", tvm_input)
- # execute
- m.run()
- # get outputs
- class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
- return class_IDs, scores, bounding_boxs
-
-
- for target in ["llvm", "cuda"]:
- dev = tvm.device(target, 0)
- if dev.exist:
- lib = build(target)
- class_IDs, scores, bounding_boxs = run(lib, dev)
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 123-124
-
-Display result
-
-.. GENERATED FROM PYTHON SOURCE LINES 124-133
-
-.. code-block:: default
-
-
- ax = utils.viz.plot_bbox(
- img,
- bounding_boxs.numpy()[0],
- scores.numpy()[0],
- class_IDs.numpy()[0],
- class_names=block.classes,
- )
- plt.show()
-
-
-
-.. image-sg:: /how_to/deploy_models/images/sphx_glr_deploy_ssd_gluoncv_001.png
- :alt: deploy ssd gluoncv
- :srcset: /how_to/deploy_models/images/sphx_glr_deploy_ssd_gluoncv_001.png
- :class: sphx-glr-single-img
-
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
- **Total running time of the script:** ( 4 minutes 3.429 seconds)
-
-
-.. _sphx_glr_download_how_to_deploy_models_deploy_ssd_gluoncv.py:
-
-.. only:: html
-
- .. container:: sphx-glr-footer sphx-glr-footer-example
-
-
- .. container:: sphx-glr-download sphx-glr-download-python
-
- :download:`Download Python source code: deploy_ssd_gluoncv.py <deploy_ssd_gluoncv.py>`
-
- .. container:: sphx-glr-download sphx-glr-download-jupyter
-
- :download:`Download Jupyter notebook: deploy_ssd_gluoncv.ipynb <deploy_ssd_gluoncv.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/how_to/deploy_models/index.rst.txt b/docs/_sources/how_to/deploy_models/index.rst.txt
index 32b75795be..f43b3b3bd5 100644
--- a/docs/_sources/how_to/deploy_models/index.rst.txt
+++ b/docs/_sources/how_to/deploy_models/index.rst.txt
@@ -184,23 +184,6 @@ backends.
</div>
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This article is an introductory tutorial to deploy SSD models with TVM. We will use GluonCV pre...">
-
-.. only:: html
-
- .. image:: /how_to/deploy_models/images/thumb/sphx_glr_deploy_ssd_gluoncv_thumb.png
- :alt: Deploy Single Shot Multibox Detector(SSD) model
-
- :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py`
-
-.. raw:: html
-
- <div class="sphx-glr-thumbnail-title">Deploy Single Shot Multibox Detector(SSD) model</div>
- </div>
-
-
.. raw:: html
</div>
@@ -219,7 +202,6 @@ backends.
/how_to/deploy_models/deploy_prequantized_tflite
/how_to/deploy_models/deploy_quantized
/how_to/deploy_models/deploy_sparse
- /how_to/deploy_models/deploy_ssd_gluoncv
diff --git a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
index 10ed41e488..d764c3dd43 100644
--- a/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/deploy_models/sg_execution_times.rst.txt
@@ -5,28 +5,26 @@
Computation times
=================
-**17:04.504** total execution time for **how_to_deploy_models** files:
+**12:06.482** total execution time for **how_to_deploy_models** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``) | 04:03.429 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:46.821 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_object_detection_pytorch.py` (``deploy_object_detection_pytorch.py``) | 03:39.216 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 02:02.477 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 02:32.486 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:28.103 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_quantized.py` (``deploy_quantized.py``) | 01:50.023 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:20.350 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized.py` (``deploy_prequantized.py``) | 01:18.960 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``) | 00:52.854 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno.py` (``deploy_model_on_adreno.py``) | 01:04.632 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:50.305 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:53.437 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_adreno_tvmc.py` (``deploy_model_on_adreno_tvmc.py``) | 00:44.970 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_android.py` (``deploy_model_on_android.py``) | 00:44.463 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:30.486 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_nano.py` (``deploy_model_on_nano.py``) | 00:29.108 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:30.109 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``) | 00:28.744 | 0.0 MB |
-+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.006 | 0.0 MB |
+| :ref:`sphx_glr_how_to_deploy_models_deploy_sparse.py` (``deploy_sparse.py``) | 00:00.007 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
index 438163577a..60bff1fe0e 100644
--- a/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/bring_your_own_datatypes.rst.txt
@@ -463,7 +463,7 @@ First let us define two helper functions to get the mobilenet model and a cat im
.. code-block:: none
- Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip04fc2a84-e2d9-46a4-967d-230faa35293c from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
+ Downloading /workspace/.mxnet/models/mobilenet0.25-9f83e440.zip79e9b89a-5a86-468f-8600-8ca3439bfd84 from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/mobilenet0.25-9f83e440.zip...
diff --git a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
index f20fec81e0..eb79a606c0 100644
--- a/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**00:56.339** total execution time for **how_to_extend_tvm** files:
+**00:57.566** total execution time for **how_to_extend_tvm** files:
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:52.410 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_bring_your_own_datatypes.py` (``bring_your_own_datatypes.py``) | 00:53.658 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.813 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_instrument.py` (``use_pass_instrument.py``) | 00:02.726 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.108 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_use_pass_infra.py` (``use_pass_infra.py``) | 00:01.175 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.008 | 0.0 MB |
+| :ref:`sphx_glr_how_to_extend_tvm_low_level_custom_pass.py` (``low_level_custom_pass.py``) | 00:00.007 | 0.0 MB |
+-------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
index 411f66bd23..407bc5bd4a 100644
--- a/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
+++ b/docs/_sources/how_to/extend_tvm/use_pass_instrument.rst.txt
@@ -220,10 +220,10 @@ profile the execution time of each passes.
.. code-block:: none
Printing results of timing profile...
- InferType: 23044us [23044us] (48.99%; 48.99%)
- FoldScaleAxis: 23992us [7us] (51.01%; 51.01%)
- FoldConstant: 23985us [1690us] (50.99%; 99.97%)
- InferType: 22295us [22295us] (47.40%; 92.96%)
+ InferType: 23147us [23147us] (48.21%; 48.21%)
+ FoldScaleAxis: 24864us [9us] (51.79%; 51.79%)
+ FoldConstant: 24856us [1824us] (51.77%; 99.96%)
+ InferType: 23031us [23031us] (47.97%; 92.66%)
@@ -262,10 +262,10 @@ Refer to following sections and :py:func:`tvm.instrument.pass_instrument` for th
.. code-block:: none
Printing results of timing profile...
- InferType: 22374us [22374us] (48.08%; 48.08%)
- FoldScaleAxis: 24162us [6us] (51.92%; 51.92%)
- FoldConstant: 24156us [1812us] (51.91%; 99.97%)
- InferType: 22344us [22344us] (48.01%; 92.50%)
+ InferType: 23251us [23251us] (48.09%; 48.09%)
+ FoldScaleAxis: 25094us [10us] (51.91%; 51.91%)
+ FoldConstant: 25084us [1895us] (51.89%; 99.96%)
+ InferType: 23190us [23190us] (47.97%; 92.45%)
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
index b66ad97ff8..348ba6ae53 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_cuda.rst.txt
@@ -331,7 +331,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 53.452991 ms
+ Convolution: 45.381633 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
index 3edada288d..6dc49c29b1 100644
--- a/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_conv_tensorcore.rst.txt
@@ -598,7 +598,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 12.238893 ms
+ conv2d with tensor core: 11.848119 ms
diff --git a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
index d9d585883a..35d027f913 100644
--- a/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/opt_gemm.rst.txt
@@ -134,8 +134,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.016416
- Baseline: 3.398832
+ Numpy running time: 0.019085
+ Baseline: 3.368684
@@ -227,7 +227,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.292257
+ Opt1: 0.311136
@@ -318,7 +318,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.329403
+ Opt2: 0.292798
@@ -406,7 +406,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.119106
+ Opt3: 0.115526
@@ -523,7 +523,7 @@ flattening.
.. code-block:: none
- Opt4: 0.108406
+ Opt4: 0.108254
@@ -635,7 +635,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.103647
+ Opt5: 0.111904
@@ -748,7 +748,7 @@ Furthermore, we can also utilize multi-core processors to do the thread-level pa
.. code-block:: none
- Opt6: 0.135706
+ Opt6: 0.132876
diff --git a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
index 28eb726472..d8a785f45d 100644
--- a/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/optimize_operators/sg_execution_times.rst.txt
@@ -5,12 +5,12 @@
Computation times
=================
-**00:34.706** total execution time for **how_to_optimize_operators** files:
+**00:34.301** total execution time for **how_to_optimize_operators** files:
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.320 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_gemm.py` (``opt_gemm.py``) | 00:31.122 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:02.033 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``) | 00:01.965 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.353 | 0.0 MB |
+| :ref:`sphx_glr_how_to_optimize_operators_opt_conv_cuda.py` (``opt_conv_cuda.py``) | 00:01.214 | 0.0 MB |
+-----------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
index ff572206a4..bc884075d8 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
Computation times
=================
-**10:26.942** total execution time for **how_to_tune_with_autoscheduler** files:
+**03:36.088** total execution time for **how_to_tune_with_autoscheduler** files:
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 06:25.662 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:30.756 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_x86.py` (``tune_network_x86.py``) | 01:45.482 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:15.659 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_cuda.py` (``tune_network_cuda.py``) | 01:13.022 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_conv2d_layer_cuda.py` (``tune_conv2d_layer_cuda.py``) | 00:17.454 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:34.055 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:16.353 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_arm.py` (``tune_network_arm.py``) | 00:14.617 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:15.764 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_network_mali.py` (``tune_network_mali.py``) | 00:14.104 | 0.0 MB |
+| :ref:`sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py` (``tune_sparse_x86.py``) | 00:00.103 | 0.0 MB |
+----------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
index e174294549..e9147cdf83 100644
--- a/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
+++ b/docs/_sources/how_to/tune_with_autoscheduler/tune_conv2d_layer_cuda.rst.txt
@@ -192,13 +192,15 @@ We can kick off the search and let the auto-scheduler do its magic.
After some measurement trials, we can load the best schedule from the log
file and apply it.
-.. GENERATED FROM PYTHON SOURCE LINES 120-129
+.. GENERATED FROM PYTHON SOURCE LINES 120-131
.. code-block:: default
# Run auto-tuning (search)
- task.tune(tune_option)
+ # We do not run the tuning in our webpage server since it takes too long.
+ # Uncomment the following line to run it by yourself.
+ # task.tune(tune_option)
# Apply the best schedule
sch, args = task.apply_best(log_file)
@@ -212,13 +214,13 @@ file and apply it.
-.. GENERATED FROM PYTHON SOURCE LINES 130-133
+.. GENERATED FROM PYTHON SOURCE LINES 132-135
We can lower the schedule to see the IR after auto-scheduling.
The auto-scheduler correctly performs optimizations including multi-level tiling,
cooperative fetching, unrolling and operator fusion.
-.. GENERATED FROM PYTHON SOURCE LINES 133-137
+.. GENERATED FROM PYTHON SOURCE LINES 135-139
.. code-block:: default
@@ -243,404 +245,490 @@ cooperative fetching, unrolling and operator fusion.
@T.prim_func
def main(data: T.Buffer((1, 512, 7, 7), "float32"), kernel: T.Buffer((512, 512, 3, 3), "float32"), bias: T.Buffer((1, 512, 1, 1), "float32"), compute: T.Buffer((1, 512, 7, 7), "float32")):
T.func_attr({"from_legacy_te_schedule": T.bool(True), "global_symbol": "main", "tir.noalias": T.bool(True)})
- blockIdx_x = T.launch_thread("blockIdx.x", 128)
- conv2d_nchw = T.allocate([4], "float32", "local")
- pad_temp_shared = T.allocate([504], "float32", "shared")
- kernel_shared = T.allocate([96], "float32", "shared")
- threadIdx_x = T.launch_thread("threadIdx.x", 49)
- conv2d_nchw_1 = T.Buffer((1,), data=conv2d_nchw, scope="local", align=4)
+ blockIdx_x = T.launch_thread("blockIdx.x", 28)
+ conv2d_nchw = T.allocate([14], "float32", "local")
+ pad_temp_shared = T.allocate([72], "float32", "shared")
+ kernel_shared = T.allocate([3072], "float32", "shared")
+ threadIdx_x = T.launch_thread("threadIdx.x", 64)
+ conv2d_nchw_1 = T.Buffer((14,), data=conv2d_nchw, scope="local", align=32)
conv2d_nchw_1[0] = T.float32(0)
conv2d_nchw_1[1] = T.float32(0)
conv2d_nchw_1[2] = T.float32(0)
conv2d_nchw_1[3] = T.float32(0)
- for rc_outer_outer in range(64):
- cse_var_1: T.int32 = rc_outer_outer * 392
+ conv2d_nchw_1[4] = T.float32(0)
+ conv2d_nchw_1[5] = T.float32(0)
+ conv2d_nchw_1[6] = T.float32(0)
+ conv2d_nchw_1[7] = T.float32(0)
+ conv2d_nchw_1[8] = T.float32(0)
+ conv2d_nchw_1[9] = T.float32(0)
+ conv2d_nchw_1[10] = T.float32(0)
+ conv2d_nchw_1[11] = T.float32(0)
+ conv2d_nchw_1[12] = T.float32(0)
+ conv2d_nchw_1[13] = T.float32(0)
+ for rc_outer_outer, ry_outer_outer in T.grid(64, 3):
+ cse_var_2: T.int32 = rc_outer_outer * 72
+ cse_var_1: T.int32 = ry_outer_outer * 3
+ pad_temp_shared_1 = T.Buffer((72,), data=pad_temp_shared, scope="shared")
+ with T.launch_thread("threadIdx.x", 64) as threadIdx_x_1:
+ data_1 = T.Buffer((25088,), data=data.data)
+ if T.likely(threadIdx_x_1 < 18):
+ pad_temp_shared_1[threadIdx_x_1 * 4] = T.if_then_else(1 <= ry_outer_outer + blockIdx_x % 7 and ry_outer_outer + blockIdx_x % 7 < 8 and 1 <= threadIdx_x_1 * 4 % 9 and threadIdx_x_1 * 4 % 9 < 8, data_1[rc_outer_outer * 392 + threadIdx_x_1 * 4 // 9 * 49 + ry_outer_outer * 7 + blockIdx_x % 7 * 7 + threadIdx_x_1 * 4 % 9 - 8], T.float32(0))
+ if T.likely(threadIdx_x_1 < 18):
+ pad_temp_shared_1[threadIdx_x_1 * 4 + 1] = T.if_then_else(1 <= ry_outer_outer + blockIdx_x % 7 and ry_outer_outer + blockIdx_x % 7 < 8 and 1 <= (threadIdx_x_1 * 4 + 1) % 9 and (threadIdx_x_1 * 4 + 1) % 9 < 8, data_1[rc_outer_outer * 392 + (threadIdx_x_1 * 4 + 1) // 9 * 49 + ry_outer_outer * 7 + blockIdx_x % 7 * 7 + (threadIdx_x_1 * 4 + 1) % 9 - 8], T.float32(0))
+ if T.likely(threadIdx_x_1 < 18):
+ pad_temp_shared_1[threadIdx_x_1 * 4 + 2] = T.if_then_else(1 <= ry_outer_outer + blockIdx_x % 7 and ry_outer_outer + blockIdx_x % 7 < 8 and 1 <= (threadIdx_x_1 * 4 + 2) % 9 and (threadIdx_x_1 * 4 + 2) % 9 < 8, data_1[rc_outer_outer * 392 + (threadIdx_x_1 * 4 + 2) // 9 * 49 + ry_outer_outer * 7 + blockIdx_x % 7 * 7 + (threadIdx_x_1 * 4 + 2) % 9 - 8], T.float32(0))
+ if T.likely(threadIdx_x_1 < 18):
+ pad_temp_shared_1[threadIdx_x_1 * 4 + 3] = T.if_then_else(1 <= ry_outer_outer + blockIdx_x % 7 and ry_outer_outer + blockIdx_x % 7 < 8 and 1 <= (threadIdx_x_1 * 4 + 3) % 9 and (threadIdx_x_1 * 4 + 3) % 9 < 8, data_1[rc_outer_outer * 392 + (threadIdx_x_1 * 4 + 3) // 9 * 49 + ry_outer_outer * 7 + blockIdx_x % 7 * 7 + (threadIdx_x_1 * 4 + 3) % 9 - 8], T.float32(0))
threadIdx_x_1 = T.env_thread("threadIdx.x")
- pad_temp_shared_1 = T.Buffer((504,), data=pad_temp_shared, scope="shared")
- data_1 = T.Buffer((25088,), data=data.data)
- with T.launch_thread(threadIdx_x_1, 49):
- pad_temp_shared_1[threadIdx_x_1 * 6] = T.if_then_else(7 < threadIdx_x_1 * 6 % 63 and threadIdx_x_1 * 6 % 63 < 56 and 1 <= threadIdx_x_1 * 6 % 7, data_1[cse_var_1 + threadIdx_x_1 * 6 // 63 * 49 + threadIdx_x_1 * 6 % 63 - 8], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 1] = T.if_then_else(7 <= (threadIdx_x_1 * 6 + 1) % 63 and (threadIdx_x_1 * 6 + 1) % 63 < 56 and 1 <= (threadIdx_x_1 * 6 + 1) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 1) // 63 * 49 + (threadIdx_x_1 * 6 + 1) % 63 - 8], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 2] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 2) % 63 and (threadIdx_x_1 * 6 + 2) % 63 < 56 and 1 <= (threadIdx_x_1 * 6 + 2) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 2) // 63 * 49 + (threadIdx_x_1 * 6 + 2) % 63 - 8], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 3] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 3) % 63 and (threadIdx_x_1 * 6 + 3) % 63 < 56 and 1 <= (threadIdx_x_1 * 6 + 3) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 3) // 63 * 49 + (threadIdx_x_1 * 6 + 3) % 63 - 8], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 4] = T.if_then_else(7 <= (threadIdx_x_1 * 6 + 4) % 63 and (threadIdx_x_1 * 6 + 4) % 63 < 56 and 1 <= (threadIdx_x_1 * 6 + 4) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 4) // 63 * 49 + (threadIdx_x_1 * 6 + 4) % 63 - 8], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 5] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 5) % 63 and (threadIdx_x_1 * 6 + 5) % 63 < 56 and 1 <= (threadIdx_x_1 * 6 + 5) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 5) // 63 * 49 + (threadIdx_x_1 * 6 + 5) % 63 - 8], T.float32(0))
- with T.launch_thread(threadIdx_x_1, 49):
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 294] = T.if_then_else(1 <= (threadIdx_x_1 * 6 // 7 + 6) % 9 and (threadIdx_x_1 * 6 // 7 + 6) % 9 < 8 and 1 <= threadIdx_x_1 * 6 % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 294) // 63 * 49 + (threadIdx_x_1 * 6 // 7 + 6) % 9 * 7 + threadIdx_x_1 * 6 % 7 - 8], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 295] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 < 8 and 1 <= (threadIdx_x_1 * 6 + 1) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 295) // 63 * 49 + ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 1) % 7 - 8], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 296] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 < 8 and 1 <= (threadIdx_x_1 * 6 + 2) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 296) // 63 * 49 + ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 2) % 7 - 8], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 297] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 < 8 and 1 <= (threadIdx_x_1 * 6 + 3) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 297) // 63 * 49 + ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 3) % 7 - 8], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 298] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 < 8 and 1 <= (threadIdx_x_1 * 6 + 4) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 298) // 63 * 49 + ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 4) % 7 - 8], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 299] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 < 8 and 1 <= (threadIdx_x_1 * 6 + 5) % 7, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 299) // 63 * 49 + ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 5) % 7 - 8], T.float32(0))
- threadIdx_x_2 = T.env_thread("threadIdx.x")
- kernel_shared_1 = T.Buffer((96,), data=kernel_shared, scope="shared")
+ kernel_shared_1 = T.Buffer((3072,), data=kernel_shared, scope="shared")
kernel_1 = T.Buffer((2359296,), data=kernel.data)
- with T.launch_thread(threadIdx_x_2, 49):
- if T.likely(threadIdx_x_2 < 48):
- kernel_shared_1[threadIdx_x_2 * 2] = kernel_1[blockIdx_x * 18432 + threadIdx_x_2 // 12 * 4608 + rc_outer_outer * 72 + threadIdx_x_2 % 12 * 6]
- if T.likely(threadIdx_x_2 < 48):
- kernel_shared_1[threadIdx_x_2 * 2 + 1] = kernel_1[blockIdx_x * 18432 + threadIdx_x_2 // 12 * 4608 + rc_outer_outer * 72 + threadIdx_x_2 % 12 * 6 + 3]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[0]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[24]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[48]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[72]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[1]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[25]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[49]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[73]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[2]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[26]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[50]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[74]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[3]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[27]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[51]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[75]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[4]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[28]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[52]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[76]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[5]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[29]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[53]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[77]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[6]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[30]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[54]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[78]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[7]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[31]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[55]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[79]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[8]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[32]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[56]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[80]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[9]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[33]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[57]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[81]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[10]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[34]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[58]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[82]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[11]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[35]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[59]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[83]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[12]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[36]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[60]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[84]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[13]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[37]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[61]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[85]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[14]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[38]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[62]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[86]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[15]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[39]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[63]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[87]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[16]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[40]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[64]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[88]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[17]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[41]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[65]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[89]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[18]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[42]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[66]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[90]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[19]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[43]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[67]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[91]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[20]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[44]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[68]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[92]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[21]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[45]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[69]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[93]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[22]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[46]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[70]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[94]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[23]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[47]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[71]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[95]
- with T.launch_thread(threadIdx_x_1, 49):
- pad_temp_shared_1[threadIdx_x_1 * 6] = T.if_then_else(7 < threadIdx_x_1 * 6 % 63 and threadIdx_x_1 * 6 % 63 < 56, data_1[cse_var_1 + threadIdx_x_1 * 6 // 63 * 49 + threadIdx_x_1 * 6 % 63 - 7], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 1] = T.if_then_else(7 <= (threadIdx_x_1 * 6 + 1) % 63 and (threadIdx_x_1 * 6 + 1) % 63 < 56, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 1) // 63 * 49 + (threadIdx_x_1 * 6 + 1) % 63 - 7], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 2] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 2) % 63 and (threadIdx_x_1 * 6 + 2) % 63 < 56, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 2) // 63 * 49 + (threadIdx_x_1 * 6 + 2) % 63 - 7], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 3] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 3) % 63 and (threadIdx_x_1 * 6 + 3) % 63 < 56, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 3) // 63 * 49 + (threadIdx_x_1 * 6 + 3) % 63 - 7], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 4] = T.if_then_else(7 <= (threadIdx_x_1 * 6 + 4) % 63 and (threadIdx_x_1 * 6 + 4) % 63 < 56, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 4) // 63 * 49 + (threadIdx_x_1 * 6 + 4) % 63 - 7], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 5] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 5) % 63 and (threadIdx_x_1 * 6 + 5) % 63 < 56, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 5) // 63 * 49 + (threadIdx_x_1 * 6 + 5) % 63 - 7], T.float32(0))
- with T.launch_thread(threadIdx_x_1, 49):
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 294] = T.if_then_else(1 <= (threadIdx_x_1 * 6 // 7 + 6) % 9 and (threadIdx_x_1 * 6 // 7 + 6) % 9 < 8, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 294) // 63 * 49 + (threadIdx_x_1 * 6 // 7 + 6) % 9 * 7 + threadIdx_x_1 * 6 % 7 - 7], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 295] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 < 8, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 295) // 63 * 49 + ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 1) % 7 - 7], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 296] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 < 8, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 296) // 63 * 49 + ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 2) % 7 - 7], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 297] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 < 8, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 297) // 63 * 49 + ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 3) % 7 - 7], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 298] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 < 8, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 298) // 63 * 49 + ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 4) % 7 - 7], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 299] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 < 8, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 299) // 63 * 49 + ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 5) % 7 - 7], T.float32(0))
- with T.launch_thread(threadIdx_x_2, 49):
- if T.likely(threadIdx_x_2 < 48):
- kernel_shared_1[threadIdx_x_2 * 2] = kernel_1[blockIdx_x * 18432 + threadIdx_x_2 // 12 * 4608 + rc_outer_outer * 72 + threadIdx_x_2 % 12 * 6 + 1]
- if T.likely(threadIdx_x_2 < 48):
- kernel_shared_1[threadIdx_x_2 * 2 + 1] = kernel_1[blockIdx_x * 18432 + threadIdx_x_2 // 12 * 4608 + rc_outer_outer * 72 + threadIdx_x_2 % 12 * 6 + 4]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[0]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[24]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[48]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[72]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[1]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[25]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[49]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[73]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[2]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[26]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[50]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[74]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[3]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[27]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[51]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[75]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[4]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[28]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[52]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[76]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[5]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[29]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[53]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[77]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[6]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[30]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[54]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[78]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[7]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[31]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[55]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[79]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[8]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[32]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[56]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[80]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[9]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[33]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[57]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[81]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[10]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[34]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[58]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[82]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[11]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[35]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[59]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[83]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[12]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[36]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[60]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[84]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[13]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[37]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[61]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 259] * kernel_shared_1[85]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[14]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[38]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[62]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 266] * kernel_shared_1[86]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[15]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[39]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[63]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 315] * kernel_shared_1[87]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[16]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[40]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[64]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 322] * kernel_shared_1[88]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[17]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[41]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[65]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 329] * kernel_shared_1[89]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[18]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[42]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[66]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 378] * kernel_shared_1[90]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[19]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[43]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[67]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 385] * kernel_shared_1[91]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[20]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[44]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[68]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 392] * kernel_shared_1[92]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[21]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[45]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[69]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 441] * kernel_shared_1[93]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[22]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[46]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[70]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 448] * kernel_shared_1[94]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[23]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[47]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[71]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 455] * kernel_shared_1[95]
- with T.launch_thread(threadIdx_x_1, 49):
- pad_temp_shared_1[threadIdx_x_1 * 6] = T.if_then_else(7 < threadIdx_x_1 * 6 % 63 and threadIdx_x_1 * 6 % 63 < 56 and threadIdx_x_1 * 6 % 7 < 6, data_1[cse_var_1 + threadIdx_x_1 * 6 // 63 * 49 + threadIdx_x_1 * 6 % 63 - 6], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 1] = T.if_then_else(7 <= (threadIdx_x_1 * 6 + 1) % 63 and (threadIdx_x_1 * 6 + 1) % 63 < 56 and (threadIdx_x_1 * 6 + 1) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 1) // 63 * 49 + (threadIdx_x_1 * 6 + 1) % 63 - 6], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 2] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 2) % 63 and (threadIdx_x_1 * 6 + 2) % 63 < 56 and (threadIdx_x_1 * 6 + 2) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 2) // 63 * 49 + (threadIdx_x_1 * 6 + 2) % 63 - 6], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 3] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 3) % 63 and (threadIdx_x_1 * 6 + 3) % 63 < 56 and (threadIdx_x_1 * 6 + 3) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 3) // 63 * 49 + (threadIdx_x_1 * 6 + 3) % 63 - 6], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 4] = T.if_then_else(7 <= (threadIdx_x_1 * 6 + 4) % 63 and (threadIdx_x_1 * 6 + 4) % 63 < 56 and (threadIdx_x_1 * 6 + 4) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 4) // 63 * 49 + (threadIdx_x_1 * 6 + 4) % 63 - 6], T.float32(0))
- pad_temp_shared_1[threadIdx_x_1 * 6 + 5] = T.if_then_else(7 < (threadIdx_x_1 * 6 + 5) % 63 and (threadIdx_x_1 * 6 + 5) % 63 < 56 and (threadIdx_x_1 * 6 + 5) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 5) // 63 * 49 + (threadIdx_x_1 * 6 + 5) % 63 - 6], T.float32(0))
- with T.launch_thread(threadIdx_x_1, 49):
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 294] = T.if_then_else(1 <= (threadIdx_x_1 * 6 // 7 + 6) % 9 and (threadIdx_x_1 * 6 // 7 + 6) % 9 < 8 and threadIdx_x_1 * 6 % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 294) // 63 * 49 + (threadIdx_x_1 * 6 // 7 + 6) % 9 * 7 + threadIdx_x_1 * 6 % 7 - 6], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 295] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 < 8 and (threadIdx_x_1 * 6 + 1) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 295) // 63 * 49 + ((threadIdx_x_1 * 6 + 1) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 1) % 7 - 6], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 296] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 < 8 and (threadIdx_x_1 * 6 + 2) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 296) // 63 * 49 + ((threadIdx_x_1 * 6 + 2) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 2) % 7 - 6], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 297] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 < 8 and (threadIdx_x_1 * 6 + 3) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 297) // 63 * 49 + ((threadIdx_x_1 * 6 + 3) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 3) % 7 - 6], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 298] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 < 8 and (threadIdx_x_1 * 6 + 4) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 298) // 63 * 49 + ((threadIdx_x_1 * 6 + 4) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 4) % 7 - 6], T.float32(0))
- if T.likely(threadIdx_x_1 < 35):
- pad_temp_shared_1[threadIdx_x_1 * 6 + 299] = T.if_then_else(1 <= ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 and ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 < 8 and (threadIdx_x_1 * 6 + 5) % 7 < 6, data_1[cse_var_1 + (threadIdx_x_1 * 6 + 299) // 63 * 49 + ((threadIdx_x_1 * 6 + 5) // 7 + 6) % 9 * 7 + (threadIdx_x_1 * 6 + 5) % 7 - 6], T.float32(0))
- with T.launch_thread(threadIdx_x_2, 49):
- if T.likely(threadIdx_x_2 < 48):
- kernel_shared_1[threadIdx_x_2 * 2] = kernel_1[blockIdx_x * 18432 + threadIdx_x_2 // 12 * 4608 + rc_outer_outer * 72 + threadIdx_x_2 % 12 * 6 + 2]
- if T.likely(threadIdx_x_2 < 48):
- kernel_shared_1[threadIdx_x_2 * 2 + 1] = kernel_1[blockIdx_x * 18432 + threadIdx_x_2 // 12 * 4608 + rc_outer_outer * 72 + threadIdx_x_2 % 12 * 6 + 5]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[0]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[24]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[48]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x] * kernel_shared_1[72]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[1]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[25]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[49]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 7] * kernel_shared_1[73]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[2]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[26]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[50]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 14] * kernel_shared_1[74]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[3]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[27]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[51]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 63] * kernel_shared_1[75]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[4]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[28]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[52]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 70] * kernel_shared_1[76]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[5]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[29]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[53]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 77] * kernel_shared_1[77]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[6]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[30]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[54]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 126] * kernel_shared_1[78]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[7]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[31]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[55]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 133] * kernel_shared_1[79]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[8]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[32]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[56]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 140] * kernel_shared_1[80]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[9]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[33]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[57]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 189] * kernel_shared_1[81]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[10]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[34]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[58]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 196] * kernel_shared_1[82]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[11]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[35]
- conv2d_nchw_1[2] = conv2d_nchw_1[2] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[59]
- conv2d_nchw_1[3] = conv2d_nchw_1[3] + pad_temp_shared_1[threadIdx_x + 203] * kernel_shared_1[83]
- conv2d_nchw_1[0] = conv2d_nchw_1[0] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[12]
- conv2d_nchw_1[1] = conv2d_nchw_1[1] + pad_temp_shared_1[threadIdx_x + 252] * kernel_shared_1[36]
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